From 279c8f19fab6342936dae55816a7a25ca5bf83a0 Mon Sep 17 00:00:00 2001
From: Angelo Ziletti <ziletti@fhi-berlin.mpg.de>
Date: Fri, 12 May 2017 07:47:09 +0200
Subject: [PATCH] New banner in LASSO_L0, Embedding, and sis_cscl.
---
beaker-notebooks/Embedding.bkr | 134 ++++++++---
beaker-notebooks/LASSO_L0.bkr | 427 ++++++++++++++++++++++-----------
beaker-notebooks/sis_cscl.bkr | 297 +++++++++++++++--------
3 files changed, 582 insertions(+), 276 deletions(-)
diff --git a/beaker-notebooks/Embedding.bkr b/beaker-notebooks/Embedding.bkr
index be4f5a3..82b32f1 100644
--- a/beaker-notebooks/Embedding.bkr
+++ b/beaker-notebooks/Embedding.bkr
@@ -47,17 +47,90 @@
],
"cells": [
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+ "id": "codeAQQJKW",
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"body": [
- "<label style=\"text-align: left; color: #20335d; font-weight: 900; font-size: 18pt; padding-top: 2em;\">",
- " Visualizing material-similarity:</label><br/><label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> Octet-binary zincblende vs. rocksalt semiconductors</label>",
- " </p>",
- " <p style=\"font-size: 15px;\">Angelo Ziletti, Ankit Kariryaa, Emre Ahmetcik, Fawzi Mohamed, Luca Ghiringhelli, and Matthias Scheffler <span style=\"font-size: smaller;\">[version 2017-16-03]</span></p>",
- " ",
- "<div style=\"padding-top: 1em;\">"
+ "<style type=\"text/css\">",
+ "/*!",
+ " * Nomad Beaker Notebook Template",
+ " *",
+ " * @copyright Copyright 2017 Fritz Haber Institute of the Max Planck Society,",
+ " * Benjamin Regler - Apache 2.0 License",
+ " * @license http://www.apache.org/licenses/LICENSE-2.0",
+ " * @author Benjamin Regler",
+ " * @version 1.0.0",
+ " *",
+ " * Licensed under the Apache License, Version 2.0 (the \"License\");",
+ " * you may not use this file except in compliance with the License.",
+ " * You may obtain a copy of the License at",
+ " * ",
+ " * http://www.apache.org/licenses/LICENSE-2.0",
+ " *",
+ " * Unless required by applicable law or agreed to in writing, software",
+ " * distributed under the License is distributed on an \"AS IS\" BASIS,",
+ " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.",
+ " * See the License for the specific language governing permissions and",
+ " * limitations under the License.",
+ " */",
+ "p{margin-bottom:1.3em}h1,h2,h3,h4{margin:1.414em 0 .5em;font-weight:inherit;line-height:1.2}h1{margin-top:0;font-size:3.998em}h2{font-size:2.827em}h3{font-size:1.999em}h4{font-size:1.414em}.font_small,small{font-size:.707em}.notebook-container{font-size:16px}.notebook-container .bkr{font-size:100%;font-weight:400;line-height:1.45;color:#333}.nomad--header h2{color:#20335d;font-weight:700;margin:0 0 .2em}.nomad--header h3{color:#20335d;font-weight:700;margin-top:0;text-indent:-1em;padding-left:1em}.nomad--header h3:before{content:\"\\2014\";padding-right:.25em}.nomad--header .nomad--description{margin:-1em 0 0 2em}.atomic-data--block,.nomad--last-updated{display:inline-block;margin-top:1em}.nomad--last-updated{color:grey;float:right;position:relative;z-index:1}.nomad--last-updated::before{bottom:-75%;content:attr(data-version);font-size:4em;font-weight:700;opacity:.2;position:absolute;right:0}.atomic-data label{display:block;font-size:medium;font-weight:700}.atomic-data--select,.chosen-container{width:100%!important}.atomic-data--select:disabled{color:#d3d3d3}.atomic-data--reset-buton{display:inline-block;margin-top:1.6em;width:100%}.modal-dialog{max-width:1000px;width:80%}.modal-header h1{font-size:2em;line-height:1.2}.modal-dialog h2{font-size:1.414em}.modal-dialog h2:first-child{margin-top:0}.modal-dialog h3{font-size:1.2em}.modal-dialog dt{font-size:larger;margin-top:1.414em}.modal-dialog img{width:100%}.modal-dialog .authors{text-transform:uppercase}",
+ "</style>"
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+ "result": {
+ "type": "BeakerDisplay",
+ "innertype": "Html",
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n/*!\n * Nomad Beaker Notebook Template\n *\n * @copyright Copyright 2017 Fritz Haber Institute of the Max Planck Society,\n * Benjamin Regler - Apache 2.0 License\n * @license http://www.apache.org/licenses/LICENSE-2.0\n * @author Benjamin Regler\n * @version 1.0.0\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\np{margin-bottom:1.3em}h1,h2,h3,h4{margin:1.414em 0 .5em;font-weight:inherit;line-height:1.2}h1{margin-top:0;font-size:3.998em}h2{font-size:2.827em}h3{font-size:1.999em}h4{font-size:1.414em}.font_small,small{font-size:.707em}.notebook-container{font-size:16px}.notebook-container .bkr{font-size:100%;font-weight:400;line-height:1.45;color:#333}.nomad--header h2{color:#20335d;font-weight:700;margin:0 0 .2em}.nomad--header h3{color:#20335d;font-weight:700;margin-top:0;text-indent:-1em;padding-left:1em}.nomad--header h3:before{content:\"\\2014\";padding-right:.25em}.nomad--header .nomad--description{margin:-1em 0 0 2em}.atomic-data--block,.nomad--last-updated{display:inline-block;margin-top:1em}.nomad--last-updated{color:grey;float:right;position:relative;z-index:1}.nomad--last-updated::before{bottom:-75%;content:attr(data-version);font-size:4em;font-weight:700;opacity:.2;position:absolute;right:0}.atomic-data label{display:block;font-size:medium;font-weight:700}.atomic-data--select,.chosen-container{width:100%!important}.atomic-data--select:disabled{color:#d3d3d3}.atomic-data--reset-buton{display:inline-block;margin-top:1.6em;width:100%}.modal-dialog{max-width:1000px;width:80%}.modal-header h1{font-size:2em;line-height:1.2}.modal-dialog h2{font-size:1.414em}.modal-dialog h2:first-child{margin-top:0}.modal-dialog h3{font-size:1.2em}.modal-dialog dt{font-size:larger;margin-top:1.414em}.modal-dialog img{width:100%}.modal-dialog .authors{text-transform:uppercase}\n</style>"
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+ "id": "codev9z3kR",
+ "type": "code",
+ "evaluator": "HTML",
+ "input": {
+ "body": [
+ "<div id=\"teaser\" style='background-color: rgba(149,170,79, 1.0); background-position: right center; background-size: 200px; background-repeat: no-repeat; ",
+ " padding-top: 20px;",
+ " padding-right: 10px;",
+ " padding-bottom: 50px;",
+ " padding-left: 80px;' > ",
+ "",
+ " <div class=\"nomad--header\">",
+ " <div style=\"text-align:center\">",
+ " <h2> <img id=\"nomad\" src=\"https://nomad-coe.eu/uploads/nomad/images/NOMAD_Logo2.png\" height=\"100\" alt=\"NOMAD Logo\"> NOMAD Analytics Toolkit ",
+ " <img id=\"nomad\" src=\"https://www.nomad-coe.eu/uploads/nomad/backgrounds/head_big-data_analytics_2.png\" height=\"80\" alt=\"NOMAD Logo\"> </h2>",
+ " </div>",
+ " <h3> Visualizing material-similarity: Octet-binary zincblende vs. rocksalt semiconductors</h3>",
+ " <p class=\"nomad--description\">",
+ " created by:",
+ " Angelo Ziletti<sup> 1</sup>,",
+ " Ankit Kariryaa<sup>1</sup>, ",
+ " Emre Ahmetcik<sup>1</sup>,",
+ " Fawzi R. Mohamed<sup>1</sup>,",
+ " Luca Ghiringhelli<sup>1</sup>,",
+ " and Matthias Scheffler<sup>1</sup> <br><br>",
+ " ",
+ " <sup>1</sup> Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany <br>",
+ " <span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: May 12, 2017]</span>",
+ " </p>",
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+ "<a href=\"https://analytics-toolkit.nomad-coe.eu/home/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to Analytics Home</a> ",
+ "<a href=\"https://www.nomad-coe.eu/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to NOMAD CoE Home</a> ",
+ "</div>"
],
"hidden": true
},
@@ -66,14 +139,14 @@
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+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<div id=\"teaser\" style=\"background-color: rgba(149,170,79, 1.0); background-position: right center; background-size: 200px; background-repeat: no-repeat; \n padding-top: 20px;\n padding-right: 10px;\n padding-bottom: 50px;\n padding-left: 80px;\"> \n\n <div class=\"nomad--header\">\n <div style=\"text-align:center\">\n <h2> <img id=\"nomad\" src=\"https://nomad-coe.eu/uploads/nomad/images/NOMAD_Logo2.png\" alt=\"NOMAD Logo\" height=\"100\"> NOMAD Analytics Toolkit \n <img id=\"nomad\" src=\"https://www.nomad-coe.eu/uploads/nomad/backgrounds/head_big-data_analytics_2.png\" alt=\"NOMAD Logo\" height=\"80\"> </h2>\n </div>\n <h3> Visualizing material-similarity: Octet-binary zincblende vs. rocksalt semiconductors</h3>\n <p class=\"nomad--description\">\n created by:\n Angelo Ziletti<sup> 1</sup>,\n Ankit Kariryaa<sup>1</sup>, \n Emre Ahmetcik<sup>1</sup>,\n Fawzi R. Mohamed<sup>1</sup>,\n Luca Ghiringhelli<sup>1</sup>,\n and Matthias Scheffler<sup>1</sup> <br><br>\n \n <sup>1</sup> Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany <br>\n <span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: May 12, 2017]</span>\n </p>\n</div>\n\n</div> \n\n<div style=\"text-align: right;\">\n<a href=\"https://analytics-toolkit.nomad-coe.eu/home/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to Analytics Home</a> \n<a href=\"https://www.nomad-coe.eu/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to NOMAD CoE Home</a> \n</div>"
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@@ -81,11 +154,6 @@
"evaluator": "HTML",
"input": {
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- "<style type=\"text/css\">",
- " .lasso_instructions{",
- " font-size: 15px;",
- " } ",
- "</style>",
"<!-- Button trigger modal -->",
"<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-motivation-modal\">",
" Introduction and motivation",
@@ -141,14 +209,14 @@
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- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n .lasso_instructions{\n font-size: 15px;\n } \n</style>\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-motivation-modal\">\n Introduction and motivation\n</button>\n\n<!-- Modal -->\n<div style=\"display: none;\" class=\"modal fade\" id=\"lasso-motivation-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-motivation-modal-label\">\n <div class=\"modal-dialog modal-lg\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-motivation-modal-label\">Introduction and motivation</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n <p> In this tutorial, we present a tool that produces two-dimensional structure maps for octet binary compounds, by starting from a high-dimensional set of <i>features</i> (coordinates) that identify each data point (material), based on free-atom data of the atomic species constituting the binary material. </p>\n \n <p> The low-dimensional embedding methods (here, two-dimensional for the sake of visualization) are <i>unsupervised</i> machine-learning algorithms; so, in our example, the algorithm processes only the spatial arrangement of the points in the high-dimensional representation that is determined by the user. </p>\n \n <p> In the linear method, <b>principal component analysis (<a href=\"https://en.wikipedia.org/wiki/Feature_scaling\" target=\"_blank\">PCA</a>)</b>, the direction (linear combination of the input coordinates) with the maximum variance is identified as the first principal component (PC). The direction perpendicular to the first PC with the largest variance is the second PC.\n The process can be iterated up to as many dimensions as the initial dimensionality of the data, but here we stop at the second dimension and give the amount of total variance recovered by the first two principal components. </p>\n <p> In the two popular non-linear method we chose, <b>multidimensional scaling (<a href=\"https://en.wikipedia.org/wiki/Multidimensional_scaling\" target=\"_blank\">MDS</a>) </b> tries to preserve the distances from the given high-dimensional to the two-dimensional representation, \n and the <b>t-Distributed Stochastic Neighbor Embedding (<a href=\"https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding\" target=\"_blank\">t-SNE</a>) </b> tries to preserve the local shape of groups of neighboring points. Both methods use a notion of distance that in our example is the Euclidean norm, even if in principle it could be any proper norm. </p>\n\n <p> In the results, we show the data points colored according to the difference in energy between the Rocksalt (RS) and Zincblende (ZB) crystal structures (both relaxed to their local minima) of the material they represent. The labeling and consequent coloring are independent of the embedding method used, therefore the labeling is an <i>a posteriori</i>\n check that the high-dimensional representation could contain information about the labeling itself. In practice, if the coloring identifies clearly distinct areas, then the two dimensional representation is a map for the prediction of the labels, so that a new data point of unknown labeling, that lands in the 2D map in a area of points with known labeling, is expected to belong to that same labeling. </p>\n \n<p>The merit of the embedding methods is to provide relatively inexpensive tools to visually test whether a given set of features contains information about an investigated property (label). For this reason, they are widely used as preliminary tools for discovering structures in the data. </p>\n<p> The prediction of RS vs ZB structure from a simple descriptor has a notable history in materials science [1-7], where descriptors were designed by chemically/physically-inspired intuition. </p>\n\n <p>References:</p>\n <ol>\n <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>\n <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>\n <li>J. St. John and A.N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>\n <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>\n <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>\n <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>\n <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>\n </ol>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-motivation-modal\">\n Introduction and motivation\n</button>\n\n<!-- Modal -->\n<div style=\"display: none;\" class=\"modal fade\" id=\"lasso-motivation-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-motivation-modal-label\">\n <div class=\"modal-dialog modal-lg\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-motivation-modal-label\">Introduction and motivation</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n <p> In this tutorial, we present a tool that produces two-dimensional structure maps for octet binary compounds, by starting from a high-dimensional set of <i>features</i> (coordinates) that identify each data point (material), based on free-atom data of the atomic species constituting the binary material. </p>\n \n <p> The low-dimensional embedding methods (here, two-dimensional for the sake of visualization) are <i>unsupervised</i> machine-learning algorithms; so, in our example, the algorithm processes only the spatial arrangement of the points in the high-dimensional representation that is determined by the user. </p>\n \n <p> In the linear method, <b>principal component analysis (<a href=\"https://en.wikipedia.org/wiki/Feature_scaling\" target=\"_blank\">PCA</a>)</b>, the direction (linear combination of the input coordinates) with the maximum variance is identified as the first principal component (PC). The direction perpendicular to the first PC with the largest variance is the second PC.\n The process can be iterated up to as many dimensions as the initial dimensionality of the data, but here we stop at the second dimension and give the amount of total variance recovered by the first two principal components. </p>\n <p> In the two popular non-linear method we chose, <b>multidimensional scaling (<a href=\"https://en.wikipedia.org/wiki/Multidimensional_scaling\" target=\"_blank\">MDS</a>) </b> tries to preserve the distances from the given high-dimensional to the two-dimensional representation, \n and the <b>t-Distributed Stochastic Neighbor Embedding (<a href=\"https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding\" target=\"_blank\">t-SNE</a>) </b> tries to preserve the local shape of groups of neighboring points. Both methods use a notion of distance that in our example is the Euclidean norm, even if in principle it could be any proper norm. </p>\n\n <p> In the results, we show the data points colored according to the difference in energy between the Rocksalt (RS) and Zincblende (ZB) crystal structures (both relaxed to their local minima) of the material they represent. The labeling and consequent coloring are independent of the embedding method used, therefore the labeling is an <i>a posteriori</i>\n check that the high-dimensional representation could contain information about the labeling itself. In practice, if the coloring identifies clearly distinct areas, then the two dimensional representation is a map for the prediction of the labels, so that a new data point of unknown labeling, that lands in the 2D map in a area of points with known labeling, is expected to belong to that same labeling. </p>\n \n<p>The merit of the embedding methods is to provide relatively inexpensive tools to visually test whether a given set of features contains information about an investigated property (label). For this reason, they are widely used as preliminary tools for discovering structures in the data. </p>\n<p> The prediction of RS vs ZB structure from a simple descriptor has a notable history in materials science [1-7], where descriptors were designed by chemically/physically-inspired intuition. </p>\n\n <p>References:</p>\n <ol>\n <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>\n <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>\n <li>J. St. John and A.N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>\n <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>\n <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>\n <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>\n <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>\n </ol>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>"
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"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar run_lasso = function() {\n $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");\n getFeatures();\n getEmbedMethod();\n getStandardize();\n getUnits();\n beaker.evaluate(\"lasso_cell\"); // evaluate cells with tag \"lasso_cell\"\n // view_result()\n};\nvar reset_lasso = function(){\n beaker.evaluate(\"lasso_gui\");\n};\nvar getFeatures = function() {\n beaker.selected_feature_list = [];\n $('#lasso_features_select input:checkbox').each(function () {\n if(this.checked )\n beaker.selected_feature_list.push(this.value);\n });\n};\n \nvar getUnits = function() {\n beaker.units = $(\"#units_select\").val();\n};\n \nvar getEmbedMethod = function() {\n beaker.embed_method = \"pca\";\n $('#embed_method_selector input:radio').each(function () {\n if(this.checked )\n beaker.embed_method = this.value;\n });\n};\n \nvar getStandardize = function() {\n beaker.standardize = \"yes\";\n $('#standardize input:radio').each(function () {\n if(this.checked )\n beaker.standardize = this.value;\n });\n};\n \nbeaker.view_result = function(result_link) {\n// beaker.evaluate(\"lasso_viewer_result\").then(function(x) {\n $(\"#lasso_result_button\").attr(\"href\", result_link);\n// }); \n $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");\n}\n</script>\n<style type=\"text/css\">\n label {\n font-size: 18px;\n }\n .lasso_control{\n font-size: 18px;\n } \n.lasso_form_group input {\n width: 15px;\n height: 15px;\n padding: 0;\n margin:0;\n padding-right:5px; \n vertical-align: bottom;\n top: -1px;\n} \n .lasso_selection_description{\n padding: 10px 15px;\n }\n</style>\n<div class=\"lasso_control\">\n <div class=\"row\">\n <p class=\"lasso_selection_description\"><b>Primary features </b>\n (hover the mouse pointer over the feature names to see a full description):</p>\n <form id=\"lasso_features_select\">\n <div class=\"lasso_form_group\">\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_ionization_potential\" checked=\"\" type=\"checkbox\"> <span title=\"Atomic ionization potential\"><i>IP</i> </span></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_electron_affinity\" checked=\"\" type=\"checkbox\"> <span title=\"Atomic electron affinity\"> <i>EA</i></span></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_homo\" type=\"checkbox\"> <span title=\"Energy of highest occupied molecular orbital\"><i>E</i> <sub>HOMO</sub></span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-2\"> <input value=\"atomic_lumo\" type=\"checkbox\"> <span title=\"Energy of lowest unoccupied molecular orbital\"> <i>E</i> <sub>LUMO</sub> </span> </label>\n \n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_rs_max\" checked=\"\" type=\"checkbox\"> <span title=\"Radius at which the radial probability density of the valence s orbital is maximum\"> <i>r</i><sub>s</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_rp_max\" checked=\"\" type=\"checkbox\"> <span title=\"Radius at which the radial probability density of the valence p orbital is maximum\"> <i>r</i><sub>p</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_rd_max\" checked=\"\" type=\"checkbox\"> <span title=\"Radius at which the radial probability density of the valence d orbital is maximum\"> <i>r</i><sub>d</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_number\" type=\"checkbox\"> <span title=\"Atomic number\"> <i>Z</i> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_number_valence_electrons\" type=\"checkbox\"> <span title=\"Number of valence electrons\"> <i>Z</i><sub>val</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"period\" type=\"checkbox\"> <span title=\"Period (in the periodic table)\"> <i>n</i> <sub>period</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_r_by_2_dimer\" type=\"checkbox\"> <span title=\"Bond length of the dimer\"> <i>d</i> <sub>dimer</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_electronic_binding_energy_dimer\" type=\"checkbox\"> <span title=\"Binding energy of the dimer\"> <i>E</i> <sub>b</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_homo_lumo_diff\" type=\"checkbox\"> <span title=\"HOMO-LUMO gap of the dimer\"> Δ<i>E</i><sub>HL</sub> </span> </label>\n \n<!--- <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"Es/sqrt(Zval)\" > \n <span title=\"Energy of the valence s orbital(s) divided by the square root of the number of valence electrons. [Phys. Rev. B 85, 104104 (2012)]\"> <i>E</i><sub>s</sub>/sqrt(<i>Z</i> <sub>val</sub>) </span> </label>\n <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"Ep/sqrt(Zval)\" > \n <span title=\"Energy of the valence p orbital(s) divided by the square root of the number of valence electrons. [Phys. Rev. B 85, 104104 (2012)]\"> <i>E</i><sub>p</sub>/sqrt(<i>Z</i> <sub>val</sub>) </span> </label>\n--> \n </div>\n </form>\n </div> <!-- End of row-->\n \n <br>\n <div class=\"row\"> <!-- Start of row-->\n <p class=\"lasso_selection_description\"><b>Units of measurement: </b> \n <select id=\"units_select\">\n <option value=\"eV_angstrom\"> [energy]=eV; [length]=angstrom</option>\n <option value=\"J_m\"> [energy]=J; [length]=m</option>\n <option value=\"kcal/mol_angstrom\"> [energy]=kcal/mol; [length]=angstrom</option>\n </select> </p>\n </div><!-- End of row-->\n \n <br>\n <div class=\"row\"> <!-- Start of second row-->\n <div class=\"lasso_form_group\">\n <p class=\"lasso_selection_description\"><b>Embedding methods:</b> </p>\n <div id=\"embed_method_selector\">\n <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptions\" id=\"inlineRadio1\" value=\"pca\" checked=\"\" type=\"radio\"> Principal Compenent Analysis (PCA) [<a href=\"https://en.wikipedia.org/wiki/Principal_component_analysis\" target=\"_blank\">more info</a>]</label>\n <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptions\" id=\"inlineRadio2\" value=\"mds\" type=\"radio\"> Multidimensional scaling (MDS) [<a href=\"https://en.wikipedia.org/wiki/Multidimensional_scaling\" target=\"_blank\">more info</a>]</label>\n <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptions\" id=\"inlineRadio3\" value=\"tsne_pca\" type=\"radio\"> t-Distributed Stochastic Neighbor Embedding (t-SNE) [<a href=\"https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding\" target=\"_blank\">more info</a>]</label>\n </div> \n </div>\n </div><!-- End of row--> \n <div class=\"row\"> <!-- Start of second row-->\n <div class=\"lasso_form_group\">\n <p class=\"lasso_selection_description\"><b>Scale data to unit-variance:</b>\n (data are centered around the mean in any case) [<a href=\"https://en.wikipedia.org/wiki/Feature_scaling\" target=\"_blank\">more info</a>]</p>\n <div id=\"standardize\">\n <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptionsStandardize\" id=\"inlineRadio4\" value=\"True\" checked=\"\" type=\"radio\"> yes </label>\n <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptionsStandardize\" id=\"inlineRadio5\" value=\"False\" type=\"radio\"> no </label>\n </div> \n </div>\n </div><!-- End of row--> \n <br>\n\n<!-- <span title=''> <img src=\"http://images.clipartpanda.com/question-purzen_Icon_with_question_mark_Vector_Clipart.png\" style=\"height: 30px; width: 30px;\"> </span> -->\n <button class=\"btn btn-default\" onclick=\"run_lasso()\">RUN TWO-DIMENSIONAL EMBEDDING</button>\n <button class=\"btn btn-default\" onclick=\"reset_lasso()\">RESET</button>\n <label title=\"This button becomes active when the run is finished. By clicking it, an interactive structural-similarity plot will be opened\"> <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\">View interactive 2D scatter plot</a> </label>\n</div> <!-- End of lasso_control -->\n"
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@@ -465,9 +533,9 @@
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@@ -575,8 +643,8 @@
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@@ -628,8 +696,8 @@
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@@ -734,8 +802,8 @@
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@@ -764,9 +832,9 @@
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@@ -801,9 +869,9 @@
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diff --git a/beaker-notebooks/LASSO_L0.bkr b/beaker-notebooks/LASSO_L0.bkr
index 16704e4..3619625 100644
--- a/beaker-notebooks/LASSO_L0.bkr
+++ b/beaker-notebooks/LASSO_L0.bkr
@@ -34,26 +34,89 @@
],
"cells": [
{
- "id": "code61yhp1",
+ "id": "codeDgHqiF",
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- "<label style=\"text-align: left; color: #20335d; font-weight: 900; font-size: 18pt; padding-top: 2em;\">",
- " Predicting energy differences between crystal structures I (large feature space):</label><br/><label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> Octet-binary zincblende vs. rocksalt semiconductors</label>",
- " </p>",
- " <p style=\"font-size: 15px;\">Angelo Ziletti, Ankit Kariryaa, Emre Ahmetcik, Fawzi Mohamed, Luca Ghiringhelli, and Matthias Scheffler <span style=\"font-size: smaller;\">[version 2017-02-03]</span></p>",
- " ",
- "<div style=\"padding-top: 1em;\">",
- "This tutorial shows how to find descriptive parameters (short formulas) that predict crystal structure, using the example of octet binary compounds that have either rocksalt (RS) or zincblende (ZB) structure. It is based on</div>",
- "<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">",
- "L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, M. Scheffler: <span style=\"font-style: italic;\">Big Data of Materials Science: Critical Role of the Descriptor</span>, Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\">[PDF]</a>",
- "</div>",
- "<div>",
- "Click on \"Run\" below to reproduce results from this publication; click \"Background\" for an explanation of the approach; or, modify \"Settings\" to produce your own results.",
+ "<style type=\"text/css\">",
+ "/*!",
+ " * Nomad Beaker Notebook Template",
+ " *",
+ " * @copyright Copyright 2017 Fritz Haber Institute of the Max Planck Society,",
+ " * Benjamin Regler - Apache 2.0 License",
+ " * @license http://www.apache.org/licenses/LICENSE-2.0",
+ " * @author Benjamin Regler",
+ " * @version 1.0.0",
+ " *",
+ " * Licensed under the Apache License, Version 2.0 (the \"License\");",
+ " * you may not use this file except in compliance with the License.",
+ " * You may obtain a copy of the License at",
+ " * ",
+ " * http://www.apache.org/licenses/LICENSE-2.0",
+ " *",
+ " * Unless required by applicable law or agreed to in writing, software",
+ " * distributed under the License is distributed on an \"AS IS\" BASIS,",
+ " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.",
+ " * See the License for the specific language governing permissions and",
+ " * limitations under the License.",
+ " */",
+ "p{margin-bottom:1.3em}h1,h2,h3,h4{margin:1.414em 0 .5em;font-weight:inherit;line-height:1.2}h1{margin-top:0;font-size:3.998em}h2{font-size:2.827em}h3{font-size:1.999em}h4{font-size:1.414em}.font_small,small{font-size:.707em}.notebook-container{font-size:16px}.notebook-container .bkr{font-size:100%;font-weight:400;line-height:1.45;color:#333}.nomad--header h2{color:#20335d;font-weight:700;margin:0 0 .2em}.nomad--header h3{color:#20335d;font-weight:700;margin-top:0;text-indent:-1em;padding-left:1em}.nomad--header h3:before{content:\"\\2014\";padding-right:.25em}.nomad--header .nomad--description{margin:-1em 0 0 2em}.atomic-data--block,.nomad--last-updated{display:inline-block;margin-top:1em}.nomad--last-updated{color:grey;float:right;position:relative;z-index:1}.nomad--last-updated::before{bottom:-75%;content:attr(data-version);font-size:4em;font-weight:700;opacity:.2;position:absolute;right:0}.atomic-data label{display:block;font-size:medium;font-weight:700}.atomic-data--select,.chosen-container{width:100%!important}.atomic-data--select:disabled{color:#d3d3d3}.atomic-data--reset-buton{display:inline-block;margin-top:1.6em;width:100%}.modal-dialog{max-width:1000px;width:80%}.modal-header h1{font-size:2em;line-height:1.2}.modal-dialog h2{font-size:1.414em}.modal-dialog h2:first-child{margin-top:0}.modal-dialog h3{font-size:1.2em}.modal-dialog dt{font-size:larger;margin-top:1.414em}.modal-dialog img{width:100%}.modal-dialog .authors{text-transform:uppercase}",
+ "</style>"
+ ],
+ "hidden": true
+ },
+ "output": {
+ "state": {},
+ "result": {
+ "type": "BeakerDisplay",
+ "innertype": "Html",
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n/*!\n * Nomad Beaker Notebook Template\n *\n * @copyright Copyright 2017 Fritz Haber Institute of the Max Planck Society,\n * Benjamin Regler - Apache 2.0 License\n * @license http://www.apache.org/licenses/LICENSE-2.0\n * @author Benjamin Regler\n * @version 1.0.0\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\np{margin-bottom:1.3em}h1,h2,h3,h4{margin:1.414em 0 .5em;font-weight:inherit;line-height:1.2}h1{margin-top:0;font-size:3.998em}h2{font-size:2.827em}h3{font-size:1.999em}h4{font-size:1.414em}.font_small,small{font-size:.707em}.notebook-container{font-size:16px}.notebook-container .bkr{font-size:100%;font-weight:400;line-height:1.45;color:#333}.nomad--header h2{color:#20335d;font-weight:700;margin:0 0 .2em}.nomad--header h3{color:#20335d;font-weight:700;margin-top:0;text-indent:-1em;padding-left:1em}.nomad--header h3:before{content:\"\\2014\";padding-right:.25em}.nomad--header .nomad--description{margin:-1em 0 0 2em}.atomic-data--block,.nomad--last-updated{display:inline-block;margin-top:1em}.nomad--last-updated{color:grey;float:right;position:relative;z-index:1}.nomad--last-updated::before{bottom:-75%;content:attr(data-version);font-size:4em;font-weight:700;opacity:.2;position:absolute;right:0}.atomic-data label{display:block;font-size:medium;font-weight:700}.atomic-data--select,.chosen-container{width:100%!important}.atomic-data--select:disabled{color:#d3d3d3}.atomic-data--reset-buton{display:inline-block;margin-top:1.6em;width:100%}.modal-dialog{max-width:1000px;width:80%}.modal-header h1{font-size:2em;line-height:1.2}.modal-dialog h2{font-size:1.414em}.modal-dialog h2:first-child{margin-top:0}.modal-dialog h3{font-size:1.2em}.modal-dialog dt{font-size:larger;margin-top:1.414em}.modal-dialog img{width:100%}.modal-dialog .authors{text-transform:uppercase}\n</style>"
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+ "evaluator": "HTML",
+ "input": {
+ "body": [
+ "<div id=\"teaser\" style='background-color: rgba(159,180,75, 0.9); background-position: right center; background-size: 200px; background-repeat: no-repeat; ",
+ " padding-top: 20px;",
+ " padding-right: 10px;",
+ " padding-bottom: 50px;",
+ " padding-left: 80px;' > ",
+ " ",
+ "<div class=\"nomad--header\">",
+ " <div style=\"text-align:center\">",
+ " <h2> <img id=\"nomad\" src=\"https://nomad-coe.eu/uploads/nomad/images/NOMAD_Logo2.png\" height=\"100\" alt=\"NOMAD Logo\"> NOMAD Analytics Toolkit ",
+ " <img id=\"nomad\" src=\"https://www.nomad-coe.eu/uploads/nomad/backgrounds/head_big-data_analytics_2.png\" height=\"80\" alt=\"NOMAD Logo\"> </h2>",
+ " </div>",
+ " <h3>Predicting energy differences between crystal structures I (large feature space): Octet-binary zincblende vs. rocksalt semiconductors</h3>",
+ " <p class=\"nomad--description\">",
+ " created by:",
+ " Angelo Ziletti<sup> 1</sup>,",
+ " Ankit Kariryaa<sup>1</sup>, ",
+ " Emre Ahmetcik<sup>1</sup>,",
+ " Fawzi R. Mohamed<sup>1</sup>,",
+ " Luca Ghiringhelli<sup>1</sup>,",
+ " and Matthias Scheffler<sup>1</sup> <br><br>",
+ " ",
+ " <sup>1</sup> Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany <br>",
+ " <span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: May 10, 2017]</span>",
+ " </p>",
"</div>",
- "<div style=\"padding-top: 2ex;\">",
- "<span style=\"font-weight: bold;\">Idea: </span> Starting from simple physical quantities (\"building blocks\", here properties of the constituent free atoms such as orbital radii), thousands of candidate formulas are generated by applying arithmetic operations combining building blocks, for example forming sums and products of them. These candidate formulas constitute the so-called \"feature space\". Then, a sparse regression method is used to select only a few of these formulas that explain the data.",
+ "",
+ "</div> ",
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+ "<a href=\"https://analytics-toolkit.nomad-coe.eu/home/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to Analytics Home</a> ",
+ "<a href=\"https://www.nomad-coe.eu/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to NOMAD CoE Home</a> ",
"</div>"
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"hidden": true
@@ -63,98 +126,53 @@
"result": {
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- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<label style=\"text-align: left; color: #20335d; font-weight: 900; font-size: 18pt; padding-top: 2em;\">\n Predicting energy differences between crystal structures I (large feature space):</label><br><label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> Octet-binary zincblende vs. rocksalt semiconductors</label>\n <p></p>\n <p style=\"font-size: 15px;\">Angelo Ziletti, Ankit Kariryaa, Emre Ahmetcik, Fawzi Mohamed, Luca Ghiringhelli, and Matthias Scheffler <span style=\"font-size: smaller;\">[version 2017-02-03]</span></p>\n \n<div style=\"padding-top: 1em;\">\nThis tutorial shows how to find descriptive parameters (short formulas) that predict crystal structure, using the example of octet binary compounds that have either rocksalt (RS) or zincblende (ZB) structure. It is based on</div>\n<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\nL. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, M. Scheffler: <span style=\"font-style: italic;\">Big Data of Materials Science: Critical Role of the Descriptor</span>, Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\">[PDF]</a>\n</div>\n<div>\nClick on \"Run\" below to reproduce results from this publication; click \"Background\" for an explanation of the approach; or, modify \"Settings\" to produce your own results.\n</div>\n<div style=\"padding-top: 2ex;\">\n<span style=\"font-weight: bold;\">Idea: </span> Starting from simple physical quantities (\"building blocks\", here properties of the constituent free atoms such as orbital radii), thousands of candidate formulas are generated by applying arithmetic operations combining building blocks, for example forming sums and products of them. These candidate formulas constitute the so-called \"feature space\". Then, a sparse regression method is used to select only a few of these formulas that explain the data.\n</div>"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<div id=\"teaser\" style=\"background-color: rgba(159,180,75, 0.9); background-position: right center; background-size: 200px; background-repeat: no-repeat; \n padding-top: 20px;\n padding-right: 10px;\n padding-bottom: 50px;\n padding-left: 80px;\"> \n \n<div class=\"nomad--header\">\n <div style=\"text-align:center\">\n <h2> <img id=\"nomad\" src=\"https://nomad-coe.eu/uploads/nomad/images/NOMAD_Logo2.png\" alt=\"NOMAD Logo\" height=\"100\"> NOMAD Analytics Toolkit \n <img id=\"nomad\" src=\"https://www.nomad-coe.eu/uploads/nomad/backgrounds/head_big-data_analytics_2.png\" alt=\"NOMAD Logo\" height=\"80\"> </h2>\n </div>\n <h3>Predicting energy differences between crystal structures I (large feature space): Octet-binary zincblende vs. rocksalt semiconductors</h3>\n <p class=\"nomad--description\">\n created by:\n Angelo Ziletti<sup> 1</sup>,\n Ankit Kariryaa<sup>1</sup>, \n Emre Ahmetcik<sup>1</sup>,\n Fawzi R. Mohamed<sup>1</sup>,\n Luca Ghiringhelli<sup>1</sup>,\n and Matthias Scheffler<sup>1</sup> <br><br>\n \n <sup>1</sup> Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany <br>\n <span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: May 10, 2017]</span>\n </p>\n</div>\n\n</div> \n\n<div style=\"text-align: right;\">\n<a href=\"https://analytics-toolkit.nomad-coe.eu/home/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to Analytics Home</a> \n<a href=\"https://www.nomad-coe.eu/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to NOMAD CoE Home</a> \n</div>"
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+ "### Introduction ",
+ "",
+ "<div style=\"padding-top: 1em;\">",
+ "This tutorial shows how to find descriptive parameters (short formulas) that predict crystal structure, using the example of octet binary compounds that have either rocksalt (RS) or zincblende (ZB) structure. It is based on</div>",
+ "<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">",
+ "L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, M. Scheffler: <span style=\"font-style: italic;\">Big Data of Materials Science: Critical Role of the Descriptor</span>, Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\">[PDF]</a>",
+ "</div>",
+ "<div>",
+ "Click on \"Run\" below to reproduce results from this publication; click \"Background\" for an explanation of the approach; or, modify \"Settings\" to produce your own results.",
+ "</div>",
+ "<div style=\"padding-top: 2ex;\">",
+ "<span style=\"font-weight: bold;\">Idea: </span> Starting from simple physical quantities (\"building blocks\", here properties of the constituent free atoms such as orbital radii), thousands of candidate formulas are generated by applying arithmetic operations combining building blocks, for example forming sums and products of them. These candidate formulas constitute the so-called \"feature space\". Then, a sparse regression method is used to select only a few of these formulas that explain the data.",
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- " <p> In this tutorial we present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material.</p>",
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+ " <p> In this tutorial we present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material.</p>",
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"",
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@@ -216,32 +232,110 @@
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- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar run_lasso = function() {\n $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");\n getFeatures();\n getOperators();\n getMaxDim();\n getUnits();\n beaker.evaluate(\"calc_cell\"); // evaluate cells with tag \"calc_cell\"\n // view_result()\n};\nvar reset_lasso = function(){\n beaker.evaluate(\"lasso-settings-cell\");\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n e.style.display = 'block';\n b.style.display = 'inline';\n};\nvar getFeatures = function() {\n beaker.selected_feature_list = [];\n $('#lasso_features_select input:checkbox').each(function () {\n if(this.checked )\n beaker.selected_feature_list.push(this.value);\n });\n};\nvar getOperators = function() {\n beaker.allowed_operations = [];\n $('#lasso_operators_select input:checkbox').each(function () {\n if(this.checked )\n beaker.allowed_operations.push(this.value);\n });\n}; \nvar getMaxDim = function() {\n beaker.max_dim = $(\"#lasso_max_dim_selector\").val();\n};\n \nvar getUnits = function() {\n beaker.units = $(\"#units_select\").val();\n};\nvar toggle_settings = function(){\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n if(e.style.display == 'block'){\n e.style.display = 'none';\n b.style.display = 'none';\n }\n else{\n e.style.display = 'block';\n b.style.display = 'inline';\n }\n};\nbeaker.view_result = function(result_link) {\n// beaker.evaluate(\"lasso_viewer_result\").then(function(x) {\n $(\"#lasso_result_button\").attr(\"href\", result_link);\n// }); \n $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");\n}\n</script>\n<style type=\"text/css\">\n .lasso_instructions{\n font-size: 15px;\n } \n</style>\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-motivation-modal\">\n Background\n</button> \n\n<!-- Modal -->\n<div class=\"modal fade\" id=\"lasso-motivation-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-motivation-modal-label\">\n <div class=\"modal-dialog modal-lg\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-motivation-modal-label\">Background</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n <p> In this tutorial we present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material.</p>\n\n <p>In this example, we address only Rocksalt (RS) and Zincblende (ZB) crystal structures, that are the most common for the material class of octet binaries. Specifically, the tool predicts the difference in total energy between RS and ZB equilibrated structures (i.e., each structure is relaxed to its local minimum).</p>\n\n <p>The prediction of RS vs ZB structure from a simple descriptor has a notable history in materials science [1-7], where descriptors were designed by chemically/physically-inspired intuition. The tool presented here allows for the machine-learning-aided automatic discovery of a descriptor and a model for the prediction of the difference in energy between RS and ZB for 82 octet binary materials.</p>\n\n <p>The tool is based on Compressed-sensing (LASSO performed on a tailor-made feature space, followed by L0-regularized minimization, click <a href=\"https://gitlab.rzg.mpg.de/nomad-lab/public-wiki/wikis/analytics/LASSO_L0\" target=\"_blank\">here</a> for more info on the LASSO+L0 method), as introduced in: </p>\n\n <p> \"Big Data of Materials Science: Critical Role of the Descriptor\". L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, and M. Scheffler Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\"> (Click here for the free access pdf) </a></p>\n\n <p> By running the tutorial with the default setting, the results of the <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a> can be recovered. In particular, by clicking on “View interactive 2D plot”, an interactive structure-map (a chart where different structures are located in different regions of a low-dimensional representation, here two dimensional) will be opened in a new tab, similar to the following (an extended version of Fig. 2 in <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a>): </p>\n \n <img style=\"width:67%;height:67%\" src=\"https://gitlab.mpcdf.mpg.de/nomad-lab/public-wiki/uploads/eb33f1415db1b6d489cbbc3e6a899942/2016-08-02_ZB_RS3-2.png\">\n <br>\n <br>\n <p> In this map the octet binaries are located via the descriptor found by our LASSO+L0 approach. The descriptor is based purely on free-atom data, namely radii of the <i>s</i> and <i>p</i> valence orbitals (rs and rp) of the atomic species and their Ionizaiton Potential and Electron Affinity (IP and EA). Materials in the red (blue) region crystallize preferably in the zincblende (rocksalt) structure. The distance to the green line is proportional to the difference in energy between the two structures. In the interactive plot accessible at the end of the learning performed by the present tool, one can obtain information on the materials by hovering and clicking on the data points. </p>\n <p>References:</p>\n <ol>\n <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>\n <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>\n <li>J. St. John and A.N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>\n <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>\n <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>\n <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>\n <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>\n </ol>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-instructions-modal\">\n Instructions\n</button> \n\n<!-- Modal -->\n<div class=\"modal fade\" id=\"lasso-instructions-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-instructions-modal-label\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-instructions-modal-label\">Instructions</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n<p>In this example, you can run a compressed-sensing based algorithm for finding the optimal descriptor (and model) that predicts the difference in energy between crystal structures (here, rocksalt vs. zincblende). </p>\n\n<p>The descriptor is selected out of a large number of candidates constructed as functions of basic input features, the primary features. </p>\n\n<p>You can select the primary features as well as which kind of unary and binary operations are allowed from the checklist below. You can also select the maximum dimensionality of the descriptor. </p>\n<p> After the wished features have been selected, click <b>RUN</b> to perform the calculations (loading the values of the primary features, creation of the feature space, and optimization via LASSO+L0). </p>\n\nDuring the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: \n <ul>\n <li> the solution (machine-learned descriptor, model, and its performance in terms of training error) is printed out underneath starting from the one-dimensional solution to the selected maximum dimensionality and</li>\n<li> the “View interactive 2D scatter plot” button unlocks; by clicking, the scatter plot with the two-dimensional descriptor appears in a separate tab. In case a dimensionality higher than 2 was selected for the descriptor, the plot displays the first two dimensions.</li>\n</ul>\n<p>Note: the plot stays active also after another run is performed, so that the output of several sets of input parameters can be compared in the viewer tabs.</p>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n\n<a target=\"_blank\" href=\"http://forum.analytics-toolkit.nomad-coe.eu/\" class=\"btn btn-primary\"> Tell us what you think</a>"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#modal-credits\" style=\"font-size:larger;\">Background</button>\n<div style=\"display: none;\" class=\"modal fade\" id=\"modal-credits\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-credits\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h1 class=\"modal-title\" id=\"modal-credits-label\">Background</h1>\n <hr>\n </div>\n <div class=\"modal-body modal-credits-body\">\n\n <p> In this tutorial we present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material.</p>\n\n <p>In this example, we address only Rocksalt (RS) and Zincblende (ZB) crystal structures, that are the most common for the material class of octet binaries. Specifically, the tool predicts the difference in total energy between RS and ZB equilibrated structures (i.e., each structure is relaxed to its local minimum).</p>\n\n <p>The prediction of RS vs ZB structure from a simple descriptor has a notable history in materials science [1-7], where descriptors were designed by chemically/physically-inspired intuition. The tool presented here allows for the machine-learning-aided automatic discovery of a descriptor and a model for the prediction of the difference in energy between RS and ZB for 82 octet binary materials.</p>\n\n <p>The tool is based on Compressed-sensing (LASSO performed on a tailor-made feature space, followed by L0-regularized minimization, click <a href=\"https://gitlab.rzg.mpg.de/nomad-lab/public-wiki/wikis/analytics/LASSO_L0\" target=\"_blank\">here</a> for more info on the LASSO+L0 method), as introduced in: </p>\n\n <p> \"Big Data of Materials Science: Critical Role of the Descriptor\". L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, and M. Scheffler Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\"> (Click here for the free access pdf) </a></p>\n\n <p> By running the tutorial with the default setting, the results of the <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a> can be recovered. In particular, by clicking on “View interactive 2D plot”, an interactive structure-map (a chart where different structures are located in different regions of a low-dimensional representation, here two dimensional) will be opened in a new tab, similar to the following (an extended version of Fig. 2 in <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a>): </p>\n \n <img style=\"width:67%;height:67%\" src=\"https://gitlab.mpcdf.mpg.de/nomad-lab/public-wiki/uploads/eb33f1415db1b6d489cbbc3e6a899942/2016-08-02_ZB_RS3-2.png\">\n <br>\n <br>\n <p> In this map the octet binaries are located via the descriptor found by our LASSO+L0 approach. The descriptor is based purely on free-atom data, namely radii of the <i>s</i> and <i>p</i> valence orbitals (rs and rp) of the atomic species and their Ionizaiton Potential and Electron Affinity (IP and EA). Materials in the red (blue) region crystallize preferably in the zincblende (rocksalt) structure. The distance to the green line is proportional to the difference in energy between the two structures. In the interactive plot accessible at the end of the learning performed by the present tool, one can obtain information on the materials by hovering and clicking on the data points. </p>\n <p>References:</p>\n <ol>\n <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>\n <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>\n <li>J. St. John and A.N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>\n <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>\n <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>\n <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>\n <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>\n </ol>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n\n<button type=\"button\" class=\"btn btn-info\" data-toggle=\"modal\" data-target=\"#modal-instructions\" style=\"font-size:larger;\">Read usage instructions</button>\n<div style=\"display: none;\" class=\"modal fade\" id=\"modal-instructions\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-instructions\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h1 class=\"modal-title\" id=\"modal-instructions-label\">Instructions</h1>\n <hr>\n </div>\n <div class=\"modal-body modal-instruction-body\">\n \n<p>In this example, you can run a compressed-sensing based algorithm for finding the optimal descriptor (and model) that predicts the difference in energy between crystal structures (here, rocksalt vs. zincblende). </p>\n\n<p>The descriptor is selected out of a large number of candidates constructed as functions of basic input features, the primary features. </p>\n\n<p>You can select the primary features as well as which kind of unary and binary operations are allowed from the checklist below. You can also select the maximum dimensionality of the descriptor. </p>\n<p> After the wished features have been selected, click <b>RUN</b> to perform the calculations (loading the values of the primary features, creation of the feature space, and optimization via LASSO+L0). </p>\n\nDuring the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: \n <ul>\n <li> the solution (machine-learned descriptor, model, and its performance in terms of training error) is printed out underneath starting from the one-dimensional solution to the selected maximum dimensionality and</li>\n<li> the “View interactive 2D scatter plot” button unlocks; by clicking, the scatter plot with the two-dimensional descriptor appears in a separate tab. In case a dimensionality higher than 2 was selected for the descriptor, the plot displays the first two dimensions.</li>\n</ul>\n<p>Note: the plot stays active also after another run is performed, so that the output of several sets of input parameters can be compared in the viewer tabs.</p>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\" style=\"font-size:larger;\">Close</button>\n </div>\n </div>\n </div>\n</div>\n\n<p></p></div>"
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+ "id": "codeGtS8VK",
+ "type": "code",
+ "evaluator": "HTML",
+ "input": {
+ "body": [
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+ "var run_lasso = function() {",
+ " $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");",
+ " getFeatures();",
+ " getOperators();",
+ " getMaxDim();",
+ " getUnits();",
+ " beaker.evaluate(\"calc_cell\"); // evaluate cells with tag \"calc_cell\"",
+ " // view_result()",
+ "};",
+ "var reset_lasso = function(){",
+ " beaker.evaluate(\"lasso-settings-cell\");",
+ " var e = document.getElementById('lasso-hidden-settings-div');",
+ " var b = document.getElementById('lasso-hidden-settings-button');",
+ " e.style.display = 'block';",
+ " b.style.display = 'inline';",
+ "};",
+ "var getFeatures = function() {",
+ " beaker.selected_feature_list = [];",
+ " $('#lasso_features_select input:checkbox').each(function () {",
+ " if(this.checked )",
+ " beaker.selected_feature_list.push(this.value);",
+ " });",
+ "};",
+ "var getOperators = function() {",
+ " beaker.allowed_operations = [];",
+ " $('#lasso_operators_select input:checkbox').each(function () {",
+ " if(this.checked )",
+ " beaker.allowed_operations.push(this.value);",
+ " });",
+ "}; ",
+ "var getMaxDim = function() {",
+ " beaker.max_dim = $(\"#lasso_max_dim_selector\").val();",
+ "};",
+ " ",
+ "var getUnits = function() {",
+ " beaker.units = $(\"#units_select\").val();",
+ "};",
+ "var toggle_settings = function(){",
+ " var e = document.getElementById('lasso-hidden-settings-div');",
+ " var b = document.getElementById('lasso-hidden-settings-button');",
+ " if(e.style.display == 'block'){",
+ " e.style.display = 'none';",
+ " b.style.display = 'none';",
+ " }",
+ " else{",
+ " e.style.display = 'block';",
+ " b.style.display = 'inline';",
+ " }",
+ "};",
+ "beaker.view_result = function(result_link) {",
+ "// beaker.evaluate(\"lasso_viewer_result\").then(function(x) {",
+ " $(\"#lasso_result_button\").attr(\"href\", result_link);",
+ "// }); ",
+ " $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");",
+ "}",
+ "</script>"
+ ],
+ "hidden": true
+ },
+ "output": {
+ "state": {},
+ "result": {
+ "type": "BeakerDisplay",
+ "innertype": "Html",
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar run_lasso = function() {\n $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");\n getFeatures();\n getOperators();\n getMaxDim();\n getUnits();\n beaker.evaluate(\"calc_cell\"); // evaluate cells with tag \"calc_cell\"\n // view_result()\n};\nvar reset_lasso = function(){\n beaker.evaluate(\"lasso-settings-cell\");\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n e.style.display = 'block';\n b.style.display = 'inline';\n};\nvar getFeatures = function() {\n beaker.selected_feature_list = [];\n $('#lasso_features_select input:checkbox').each(function () {\n if(this.checked )\n beaker.selected_feature_list.push(this.value);\n });\n};\nvar getOperators = function() {\n beaker.allowed_operations = [];\n $('#lasso_operators_select input:checkbox').each(function () {\n if(this.checked )\n beaker.allowed_operations.push(this.value);\n });\n}; \nvar getMaxDim = function() {\n beaker.max_dim = $(\"#lasso_max_dim_selector\").val();\n};\n \nvar getUnits = function() {\n beaker.units = $(\"#units_select\").val();\n};\nvar toggle_settings = function(){\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n if(e.style.display == 'block'){\n e.style.display = 'none';\n b.style.display = 'none';\n }\n else{\n e.style.display = 'block';\n b.style.display = 'inline';\n }\n};\nbeaker.view_result = function(result_link) {\n// beaker.evaluate(\"lasso_viewer_result\").then(function(x) {\n $(\"#lasso_result_button\").attr(\"href\", result_link);\n// }); \n $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");\n}\n</script>"
+ },
+ "selectedType": "BeakerDisplay",
+ "elapsedTime": 0,
+ "height": 50
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{
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@@ -315,15 +409,7 @@
" <option value=\"kcal/mol_angstrom\" > [energy]=kcal/mol; [length]=angstrom</option>",
" </select> </p>",
" </div><!-- End of row-->",
- " <div class=\"lasso_control\">",
- " <p style=\"margin-top: 1ex;\"></p>",
- " <button class=\"btn btn-default\" onclick='run_lasso()' style=\"font-weight: bold;\">RUN</button> ",
- " <button class=\"btn btn-default\" onclick='reset_lasso()'>RESET</button> ",
- " <label title=\"This button becomes active when the",
- " run is finished. By clicking it, an interactive plot of the first 2",
- " dimensions of the optimized descriptor will be opened\"> ",
- " <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\" >View interactive 2D scatter plot</a> </label>",
- " </div>",
+ " ",
"</div>"
],
"hidden": true
@@ -333,26 +419,61 @@
"result": {
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Rev. Lett. 33. 1095 (1974)]\"> r<sub>σ</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"r_pi\" type=\"checkbox\"> <span title=\"John-Bloch's indicator2: |rp(A) - rs(A)| +| rp(B) -rs(B)| \n [Phys. Rev. Lett. 33. 1095 (1974)]\"> r<sub>π</sub> </span> </label>\n </div>\n </form>\n </div> <!-- End of row-->\n <div class=\"row\"> <!-- Start of second row-->\n <p class=\"lasso_selection_description\"><b>Allowed operations:</b> <br>\n Given features x and y, apply these operations:</p>\n <form id=\"lasso_operators_select\">\n <div class=\"lasso_form_group\">\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"+\" checked=\"\" type=\"checkbox\"> x+y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"-\" type=\"checkbox\"> x-y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"|-|\" checked=\"\" type=\"checkbox\"> |x-y| </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"*\" type=\"checkbox\"> x · y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"/\" checked=\"\" type=\"checkbox\"> x/y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"^2\" checked=\"\" type=\"checkbox\"> x^2 </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"^3\" type=\"checkbox\"> x^3 </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"exp\" checked=\"\" type=\"checkbox\"> exp(x) </label>\n </div>\n </form>\n </div> <!-- End of row-->\n <div class=\"row\"> <!-- Start of third row-->\n <p class=\"lasso_selection_description\"><b>Optimal descriptor maximum dimension: </b> \n <select id=\"lasso_max_dim_selector\">\n <option value=\"2\"> 2D</option>\n <option value=\"3\"> 3D</option>\n <option value=\"4\"> 4D</option>\n <option value=\"5\"> 5D</option>\n </select> </p>\n </div><!-- End of row-->\n <div class=\"row\"> <!-- Start of forth row-->\n <p class=\"lasso_selection_description\"><b>Units of measurement: </b> \n <select id=\"units_select\">\n <option value=\"eV_angstrom\"> [energy]=eV; [length]=angstrom</option>\n <option value=\"J_m\"> [energy]=J; [length]=m</option>\n <option value=\"kcal/mol_angstrom\"> [energy]=kcal/mol; [length]=angstrom</option>\n </select> </p>\n </div><!-- End of row-->\n <div class=\"lasso_control\">\n <p style=\"margin-top: 1ex;\"></p>\n <button class=\"btn btn-default\" onclick=\"run_lasso()\" style=\"font-weight: bold;\">RUN</button> \n <button class=\"btn btn-default\" onclick=\"reset_lasso()\">RESET</button> \n <label title=\"This button becomes active when the\n run is finished. By clicking it, an interactive plot of the first 2\n dimensions of the optimized descriptor will be opened\"> \n <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\">View interactive 2D scatter plot</a> </label>\n </div>\n</div>"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n .lasso_control{\n margin-left: 10px;\n }\n</style>\n<div class=\"lasso_control\" id=\"lasso-hidden-settings-div\">\n <div class=\"row\">\n <p class=\"lasso_selection_description\"><b>Primary features </b>\n (hover the mouse\npointer over the feature names to see a full description):</p>\n <form id=\"lasso_features_select\">\n <div class=\"lasso_form_group\">\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_ionization_potential\" checked=\"\" type=\"checkbox\"> <span title=\"Atomic ionization potential\"><i>IP</i> </span></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_electron_affinity\" checked=\"\" type=\"checkbox\"> <span title=\"Atomic electron affinity\"> <i>EA</i></span></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_homo\" checked=\"\" type=\"checkbox\"> <span title=\"Energy of highest occupied molecular orbital\"><i>E</i> <sub>HOMO</sub></span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_lumo\" checked=\"\" type=\"checkbox\"> <span title=\"Energy of lowest unoccupied molecular orbital\"> <i>E</i> <sub>LUMO</sub> </span> </label>\n \n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_rs_max\" checked=\"\" type=\"checkbox\"> <span title=\"Radius at which the radial probability density of the valence s orbital is maximum\"> <i>r</i><sub>s</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_rp_max\" checked=\"\" type=\"checkbox\"> <span title=\"Radius at which the radial probability density of the valence p orbital is maximum\"> <i>r</i><sub>p</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_rd_max\" checked=\"\" type=\"checkbox\"> <span title=\"Radius at which the radial probability density of the valence d orbital is maximum\"> <i>r</i><sub>d</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_number\" type=\"checkbox\"> <span title=\"Atomic number\"> <i>Z</i> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_number_valence_electrons\" type=\"checkbox\"> <span title=\"Number of valence electrons\"> <i>Z</i><sub>val</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"period\" type=\"checkbox\"> <span title=\"Period (in the periodic table)\"> <i>n</i> <sub>period</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_r_by_2_dimer\" type=\"checkbox\"> <span title=\"Bond length of the dimer\"> <i>d</i> <sub>dimer</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_electronic_binding_energy_dimer\" type=\"checkbox\"> <span title=\"Binding energy of the dimer\"> <i>E</i> <sub>b</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_homo_lumo_diff\" type=\"checkbox\"> <span title=\"HOMO-LUMO gap of the dimer\"> Δ<i>E</i><sub>HL</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"r_sigma\" type=\"checkbox\"> <span title=\"John-Bloch's indicator1: |rp(A) + rs(A) - rp(B) -rs(B)| \n [Phys. Rev. Lett. 33. 1095 (1974)]\"> r<sub>σ</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"r_pi\" type=\"checkbox\"> <span title=\"John-Bloch's indicator2: |rp(A) - rs(A)| +| rp(B) -rs(B)| \n [Phys. Rev. Lett. 33. 1095 (1974)]\"> r<sub>π</sub> </span> </label>\n </div>\n </form>\n </div> <!-- End of row-->\n <div class=\"row\"> <!-- Start of second row-->\n <p class=\"lasso_selection_description\"><b>Allowed operations:</b> <br>\n Given features x and y, apply these operations:</p>\n <form id=\"lasso_operators_select\">\n <div class=\"lasso_form_group\">\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"+\" checked=\"\" type=\"checkbox\"> x+y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"-\" type=\"checkbox\"> x-y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"|-|\" checked=\"\" type=\"checkbox\"> |x-y| </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"*\" type=\"checkbox\"> x · y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"/\" checked=\"\" type=\"checkbox\"> x/y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"^2\" checked=\"\" type=\"checkbox\"> x^2 </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"^3\" type=\"checkbox\"> x^3 </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"exp\" checked=\"\" type=\"checkbox\"> exp(x) </label>\n </div>\n </form>\n </div> <!-- End of row-->\n <div class=\"row\"> <!-- Start of third row-->\n <p class=\"lasso_selection_description\"><b>Optimal descriptor maximum dimension: </b> \n <select id=\"lasso_max_dim_selector\">\n <option value=\"2\"> 2D</option>\n <option value=\"3\"> 3D</option>\n <option value=\"4\"> 4D</option>\n <option value=\"5\"> 5D</option>\n </select> </p>\n </div><!-- End of row-->\n <div class=\"row\"> <!-- Start of forth row-->\n <p class=\"lasso_selection_description\"><b>Units of measurement: </b> \n <select id=\"units_select\">\n <option value=\"eV_angstrom\"> [energy]=eV; [length]=angstrom</option>\n <option value=\"J_m\"> [energy]=J; [length]=m</option>\n <option value=\"kcal/mol_angstrom\"> [energy]=kcal/mol; [length]=angstrom</option>\n </select> </p>\n </div><!-- End of row-->\n \n</div>"
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- "height": 391
+ "height": 419
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- "lineCount": 76,
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"tags": "lasso-settings"
},
+ {
+ "id": "codeHuySDr",
+ "type": "code",
+ "evaluator": "HTML",
+ "input": {
+ "body": [
+ "<div class=\"lasso_control\">",
+ " <button type=\"button\" class=\"btn btn-info\" style=\"font-size:larger; color: #ffffff; font-weight: bold;\" onclick='run_lasso()'>RUN</button>",
+ "",
+ "<button type=\"button\" class=\"btn btn-default\" style=\"font-size:larger;\" onclick='reset_lasso()' >Reset</button>",
+ "",
+ "<label title=\"This button becomes active when the",
+ "run is finished. By clicking it, an interactive plot of the first 2",
+ "dimensions of the optimized descriptor will be opened\"> ",
+ "<a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" style=\"font-size:larger;\" id=\"lasso_result_button\" >View interactive 2D scatter plot</a> </label>",
+ "",
+ " </div>"
+ ],
+ "hidden": true
+ },
+ "output": {
+ "state": {},
+ "result": {
+ "type": "BeakerDisplay",
+ "innertype": "Html",
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<div class=\"lasso_control\">\n <button type=\"button\" class=\"btn btn-info\" style=\"font-size:larger; color: #ffffff; font-weight: bold;\" onclick=\"run_lasso()\">RUN</button>\n\n<button type=\"button\" class=\"btn btn-default\" style=\"font-size:larger;\" onclick=\"reset_lasso()\">Reset</button>\n\n<label title=\"This button becomes active when the\nrun is finished. By clicking it, an interactive plot of the first 2\ndimensions of the optimized descriptor will be opened\"> \n<a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" style=\"font-size:larger;\" id=\"lasso_result_button\">View interactive 2D scatter plot</a> </label>\n\n </div>"
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- "<p style=\"text-align: left; color: #aa3311; font-weight: 900; font-size: 18pt;\"> Note:</p>",
- "<p style=\"text-align: left; color: #aa3311; font-weight: 400; font-size: 12pt;\"> you will automatically generate atomic descriptors</i>, select an <i>optimal combination</i> among <i>thousands</i> of candidates, and produce a <i>fully-interactive plot</i> of your results. <br>",
+ "<p style=\"text-align: left; color: #aa3311; font-weight: 900; font-size: 20pt;\"> Note</p>",
+ "<p style=\"text-align: left; color: #aa3311; font-weight: 400; font-size: 16pt;\"> You will automatically generate atomic descriptors</i>, select an <i>optimal combination</i> among <i>thousands</i> of candidates, and produce a <i>fully-interactive plot</i> of your results. <br>",
"With the Default settings you will reproduce our publication cited above. <br>",
- "Please understand that the whole procedure may take a few moments.</p>"
+ "Please understand that the whole procedure may take a few moments.</p>",
+ ""
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@@ -361,14 +482,14 @@
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+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<p style=\"text-align: left; color: #aa3311; font-weight: 900; font-size: 20pt;\"> Note</p>\n<p style=\"text-align: left; color: #aa3311; font-weight: 400; font-size: 16pt;\"> You will automatically generate atomic descriptors, select an <i>optimal combination</i> among <i>thousands</i> of candidates, and produce a <i>fully-interactive plot</i> of your results. <br>\nWith the Default settings you will reproduce our publication cited above. <br>\nPlease understand that the whole procedure may take a few moments.</p>\n"
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"tags": "calc_cell"
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+ "evaluator": "HTML",
+ "input": {
+ "body": [
+ "<h3 style=\"margin-top: 0;\">Feedback</h3>",
+ "<p>Want to give feedback?<br /><br />",
+ " <a target=\"_blank\" href=\"https://nomad-forum.rz-berlin.mpg.de\" class=\"btn btn-primary\" style=\"font-size: larger\">Send your feedback to the analytics-toolkit forum</a>",
+ "</p>",
+ "<p>Your comments are invaluable in helping us to provide a user-friendly experience for all!<p>"
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diff --git a/beaker-notebooks/sis_cscl.bkr b/beaker-notebooks/sis_cscl.bkr
index cc4fb34..20b369d 100644
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+++ b/beaker-notebooks/sis_cscl.bkr
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{
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- " Predicting energy differences between crystal structures</label><br/><label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> (Meta-)stability of octet-binary compounds</label>",
- " </p>",
- " <p style=\"font-size: 15px;\">Angelo Ziletti, Emre Ahmetcik, Runhai Ouyang, Ankit Kariryaa, Fawzi Mohamed, Luca Ghiringhelli, Matthias Scheffler [version 2017-03-03]<span style=\"font-size: smaller;\"></span></p>",
- " ",
- "<div style=\"padding-top: 1em;\">",
- "This tutorial shows how to find descriptive parameters (short formulas) that predict the crystal structure (here, rocksalt (RS), zincblende (ZB), CsCl, NiAs or CrB), using the example of octet binary compounds. It is based on the algorithm Sure Independence Screening followed by L0 minimization (SIS+L0), that enables to search for optimal descriptor by scanning huge feature spaces.",
- " <div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">",
- "R. Ouyang, E. Ahmetcik, L. M. Ghiringhelli, and M. Scheffler: <span style=\"font-style: italic;\">Descriptor identification for material properties via compressed sensing</span>, in preparation.",
- "</div>",
- "With the default settings, the method reproduces the same results from:",
- "<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">",
- "L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, M. Scheffler: <span style=\"font-style: italic;\">Big Data of Materials Science: Critical Role of the Descriptor</span>, Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\">[PDF]</a>,",
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+ " * @license http://www.apache.org/licenses/LICENSE-2.0",
+ " * @author Benjamin Regler",
+ " * @version 1.0.0",
+ " *",
+ " * Licensed under the Apache License, Version 2.0 (the \"License\");",
+ " * you may not use this file except in compliance with the License.",
+ " * You may obtain a copy of the License at",
+ " * ",
+ " * http://www.apache.org/licenses/LICENSE-2.0",
+ " *",
+ " * Unless required by applicable law or agreed to in writing, software",
+ " * distributed under the License is distributed on an \"AS IS\" BASIS,",
+ " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.",
+ " * See the License for the specific language governing permissions and",
+ " * limitations under the License.",
+ " */",
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+ "type": "code",
+ "evaluator": "HTML",
+ "input": {
+ "body": [
+ "<div id=\"teaser\" style='background-color: rgba(149,170,79, 1.0); background-position: right center; background-size: 200px; background-repeat: no-repeat; ",
+ " padding-top: 20px;",
+ " padding-right: 10px;",
+ " padding-bottom: 50px;",
+ " padding-left: 80px;' > ",
+ "",
+ " <div class=\"nomad--header\">",
+ " <div style=\"text-align:center\">",
+ " <h2> <img id=\"nomad\" src=\"https://nomad-coe.eu/uploads/nomad/images/NOMAD_Logo2.png\" height=\"100\" alt=\"NOMAD Logo\"> NOMAD Analytics Toolkit ",
+ " <img id=\"nomad\" src=\"https://www.nomad-coe.eu/uploads/nomad/backgrounds/head_big-data_analytics_2.png\" height=\"80\" alt=\"NOMAD Logo\"> </h2>",
+ " </div>",
+ " <h3>Predicting energy differences between crystal structures: (Meta-)stability of octet-binary compounds</h3>",
+ " <p class=\"nomad--description\">",
+ " created by:",
+ " Angelo Ziletti<sup> 1</sup>,",
+ " Emre Ahmetcik<sup>1</sup>,",
+ " Runhai Ouyang<sup>1</sup>,",
+ " Ankit Kariryaa<sup>1</sup>, ",
+ " Fawzi R. Mohamed<sup>1</sup>,",
+ " Luca Ghiringhelli<sup>1</sup>,",
+ " and Matthias Scheffler<sup>1</sup> <br><br>",
+ " ",
+ " <sup>1</sup> Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany <br>",
+ " <span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: May 12, 2017]</span>",
+ " </p>",
"</div>",
- "that were obtained obtained by applying the <a href=\"http://analytics-toolkit.nomad-coe.eu/tutorial-LASSO_L0\">[LASSO+L0 algorithm]</a>. Click on <b>Run</b> below to reproduce results from this publication; click <b>Background</b> for an explanation of the approach; or, modify <b>Settings</b> to produce your own results.",
+ "",
+ "</div> ",
+ "",
+ "<div style='text-align: right;'>",
+ "<a href=\"https://analytics-toolkit.nomad-coe.eu/home/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to Analytics Home</a> ",
+ "<a href=\"https://www.nomad-coe.eu/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to NOMAD CoE Home</a> ",
"</div>",
- "<div style=\"padding-top: 2ex;\">",
- "<span style=\"font-weight: bold;\">Idea: </span> Starting from simple physical quantities (\"building blocks\", here properties of the constituent free atoms such as orbital radii), millions (or billions) of candidate formulas are generated by applying arithmetic operations combining building blocks, for example forming sums and products of them. These candidate formulas constitute the so-called \"feature space\". Then a feature selection method (SIS+L0) is used to select only a few of these formulas that explain the data.",
- "</div>"
+ ""
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@@ -66,17 +128,37 @@
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+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<div id=\"teaser\" style=\"background-color: rgba(149,170,79, 1.0); background-position: right center; background-size: 200px; background-repeat: no-repeat; \n padding-top: 20px;\n padding-right: 10px;\n padding-bottom: 50px;\n padding-left: 80px;\"> \n\n <div class=\"nomad--header\">\n <div style=\"text-align:center\">\n <h2> <img id=\"nomad\" src=\"https://nomad-coe.eu/uploads/nomad/images/NOMAD_Logo2.png\" alt=\"NOMAD Logo\" height=\"100\"> NOMAD Analytics Toolkit \n <img id=\"nomad\" src=\"https://www.nomad-coe.eu/uploads/nomad/backgrounds/head_big-data_analytics_2.png\" alt=\"NOMAD Logo\" height=\"80\"> </h2>\n </div>\n <h3>Predicting energy differences between crystal structures: (Meta-)stability of octet-binary compounds</h3>\n <p class=\"nomad--description\">\n created by:\n Angelo Ziletti<sup> 1</sup>,\n Emre Ahmetcik<sup>1</sup>,\n Runhai Ouyang<sup>1</sup>,\n Ankit Kariryaa<sup>1</sup>, \n Fawzi R. Mohamed<sup>1</sup>,\n Luca Ghiringhelli<sup>1</sup>,\n and Matthias Scheffler<sup>1</sup> <br><br>\n \n <sup>1</sup> Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany <br>\n <span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: May 10, 2017]</span>\n </p>\n</div>\n\n</div> \n\n<div style=\"text-align: right;\">\n<a href=\"https://analytics-toolkit.nomad-coe.eu/home/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to Analytics Home</a> \n<a href=\"https://www.nomad-coe.eu/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to NOMAD CoE Home</a> \n</div>\n"
},
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+ "### Introduction ",
+ "",
+ "This tutorial shows how to find descriptive parameters (short formulas) that predict the crystal structure (here, rocksalt (RS), zincblende (ZB), CsCl, NiAs or CrB), using the example of octet binary compounds. It is based on the algorithm Sure Independence Screening followed by L0 minimization (SIS+L0), that enables to search for optimal descriptor by scanning huge feature spaces.",
+ " <div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">",
+ "R. Ouyang, E. Ahmetcik, L. M. Ghiringhelli, and M. Scheffler: <span style=\"font-style: italic;\">Descriptor identification for material properties via compressed sensing</span>, in preparation.",
+ "</div>",
+ "With the default settings, the method reproduces the same results from:",
+ "<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">",
+ "L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, M. Scheffler: <span style=\"font-style: italic;\">Big Data of Materials Science: Critical Role of the Descriptor</span>, Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\">[PDF]</a>,",
+ "</div>",
+ "that were obtained obtained by applying the <a href=\"http://analytics-toolkit.nomad-coe.eu/tutorial-LASSO_L0\">[LASSO+L0 algorithm]</a>. Click on <b>Run</b> below to reproduce results from this publication; click <b>Background</b> for an explanation of the approach; or, modify <b>Settings</b> to produce your own results.",
+ "",
+ "<b> Idea: </b> Starting from simple physical quantities (\"building blocks\", here properties of the constituent free atoms such as orbital radii), millions (or billions) of candidate formulas are generated by applying arithmetic operations combining building blocks, for example forming sums and products of them. These candidate formulas constitute the so-called \"feature space\". Then a feature selection method (SIS+L0) is used to select only a few of these formulas that explain the data."
+ ],
+ "evaluatorReader": false
+ },
+ {
+ "id": "codeHb7sIB",
"type": "code",
"evaluator": "HTML",
"input": {
@@ -135,33 +217,20 @@
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"",
- "<!-- Modal -->",
- "<div class=\"modal fade\" id=\"lasso-motivation-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-motivation-modal-label\">",
- " <div class=\"modal-dialog modal-lg\" role=\"document\">",
+ "<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#modal-credits\" style=\"font-size:larger;\">Background</button>",
+ "<div class=\"modal fade\" id=\"modal-credits\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-credits\">",
+ " <div class=\"modal-dialog\" role=\"document\">",
" <div class=\"modal-content\">",
" <div class=\"modal-header\">",
" <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>",
- " <h4 class=\"modal-title\" id=\"lasso-motivation-modal-label\">Background</h4>",
+ " <h1 class=\"modal-title\" id=\"modal-credits-label\">Background</h1>",
+ " <hr>",
" </div>",
- " <div class=\"modal-body lasso_instructions\">",
- " <p>We present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material. We apply a newly developed method: Sure Independence Screening + L0-regularization (SIS+L0), that allows to find an optimal descriptor in a huge feature space containing billions of features §.",
+ " <div class=\"modal-body modal-credits-body\">",
+ "",
+ " <p>We present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material. We apply a newly developed method: Sure Independence Screening + L0-regularization (SIS+L0), that allows to find an optimal descriptor in a huge feature space containing billions of features §.",
"The method is described in:",
"<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">",
"R. Ouyang, E. Ahmetcik, L. M. Ghiringhelli, and M. Scheffler: <span style=\"font-style: italic;\">Descriptor identification for material properties via compressed sensing</span>, in preparation.",
@@ -176,7 +245,7 @@
"",
" <p> By running the tutorial with the default setting, the (RS vs. ZB) results of the <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a> identified by the LASSO+L0 method can be recovered.</p>",
"",
- "<p>§ The same task is also addressed in the tutorial <a href=\"http://analytics-toolkit.nomad-coe.eu/tutorial-LASSO_L0\">\"[Predicting energy differences between different crystal structures I: large feature space]\"</a>, where an alternative method, LASSO+L0, is used to find an optimal descriptor in a moderately large feature space. LASSO+L0 was introduced in [PRL 2015] and SIS+L0 was introduced more recently in order to cope with much larger and highly correlated feature spaces.</p>",
+ "<p>§ The same task is also addressed in the tutorial <a href=\"http://analytics-toolkit.nomad-coe.eu/tutorial-LASSO_L0\">\"[Predicting energy differences between different crystal structures I: large feature space]\"</a>, where an alternative method, LASSO+L0, is used to find an optimal descriptor in a moderately large feature space. LASSO+L0 was introduced in [<a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a>] and SIS+L0 was introduced more recently in order to cope with much larger and highly correlated feature spaces.</p>",
"",
" <p>References:</p>",
" <ol>",
@@ -187,43 +256,41 @@
" <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>",
" <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>",
" <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>",
- " </ol>",
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+ " ",
+ " ",
+ " ",
" </div>",
" <div class=\"modal-footer\">",
- " <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>",
- "<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->",
+ " <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\" style=\"font-size:larger;\">Close</button>",
" </div>",
" </div>",
" </div>",
"</div>",
"",
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"",
- "<!-- Modal -->",
- "<div class=\"modal fade\" id=\"lasso-instructions-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-instructions-modal-label\">",
+ "<button type=\"button\" class=\"btn btn-info\" data-toggle=\"modal\" data-target=\"#modal-instructions\" style=\"font-size:larger;\">Read usage instructions</button>",
+ "<div class=\"modal fade\" id=\"modal-instructions\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-instructions\">",
" <div class=\"modal-dialog\" role=\"document\">",
" <div class=\"modal-content\">",
" <div class=\"modal-header\">",
" <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>",
- " <h4 class=\"modal-title\" id=\"lasso-instructions-modal-label\">Instructions</h4>",
+ " <h1 class=\"modal-title\" id=\"modal-instructions-label\">Instructions</h1>",
+ " <hr>",
" </div>",
- " <div class=\"modal-body lasso_instructions\">",
- "<p>In this example, you can run a compressed-sensing based algorithm for finding the optimal descriptor and model that predicts the difference in energy between crystal structures (here, zincblende vs. rocksalt, CsCl, NiAs or CrB structure). </p>",
+ " <div class=\"modal-body modal-instruction-body\">",
+ " ",
+ " <p>In this example, you can run a compressed-sensing based algorithm for finding the optimal descriptor and model that predicts the difference in energy between crystal structures (here, zincblende vs. rocksalt, CsCl, NiAs or CrB structure). </p>",
"",
"<p>The descriptor is selected out of a large number of candidates constructed as functions of basic input features, the primary features. </p>",
"",
"<p>By clicking <b>Settings</b> you can select the structure pair of interest (either RS/ZB, CsCl/ZB, NiAs/ZB or CrB/ZB), the primary features as well as which kind of unary and binary operations are allowed during feature space construction from the checklist below. Moreover the dimension of the output energies (kcal/mol or eV) and the following three parameters of the SIS+L0 algorithm can be specified: ",
" <ul>",
- " <li>Number of iterations for the construction for the feature space: How often the selected operations are applied to build the feature space. At each step the opreations are applied on all features created until the current step. </li>",
- " <li>Maximum dimension of optimal descriptor: Number of SIS+SA iterations.</li>",
+ " <li>Number of iterations for the construction for the feature space: How often the selected operations are applied to build the feature space. At each step the operations are applied on all features created until the current step. </li>",
+ " <li>Maximum dimension of optimal descriptor: Number of SIS iterations.</li>",
" <li>Number of collected features per SIS iteration.</li>",
" </ul> ",
- " ",
- "</p>",
- " ",
+ " ",
" ",
"<p> After the preferred settings have been adjusted, click <b>RUN</b> for performing the calculations (loading the values of the primary features, creation of the feature space, and optimization via SIS+L0). </p>",
"",
@@ -233,23 +300,16 @@
"<li> the “View interactive 2D scatter plot” button unlocks. By clicking this button, the scatter plot with the two-dimensional descriptor appears in a separate tab. If a descriptor dimensionality greater than two was selected, the scatter plot displays the two-dimensional descriptor.</li>",
"</ul>",
"<p>Note: the scatter plot remains active even if another run is performed, which enables the output of several sets of input parameters to be compared.</p>",
+ " ",
" </div>",
" <div class=\"modal-footer\">",
- " <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>",
- "<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->",
+ " <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\" style=\"font-size:larger;\">Close</button>",
" </div>",
" </div>",
" </div>",
"</div>",
"",
- "<!-- Button trigger modal ",
- "<button type=\"button\" class=\"btn btn-default\" onclick='toggle_settings()'>",
- " Settings",
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- "",
- "<a target=\"_blank\" href=\"https://nomad-forum.rz-berlin.mpg.de/\" class=\"btn btn-primary\"> Tell us what you think</a>",
- "",
- ""
+ "<p></p>"
],
"hidden": true
},
@@ -258,14 +318,14 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar run_lasso = function() {\n $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");\n getFeatures();\n getOperators();\n beaker.max_dim = $(\"#lasso_max_dim_selector\").val();\n beaker.structures_diff = $(\"#lasso_structures_diff\").val();\n beaker.n_comb = $(\"#n_comb\").val();\n beaker.n_sis = $(\"#n_sis\").val();\n beaker.units = $(\"#units_select\").val();\n beaker.evaluate(\"calc_cell\"); // evaluate cells with tag \"lasso_cell\"\n // view_result()\n};\nvar reset_lasso = function(){\n beaker.evaluate(\"lasso-settings-cell\");\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n e.style.display = 'block';\n b.style.display = 'inline';\n};\nvar getFeatures = function() {\n beaker.selected_feature_list = [];\n $('#lasso_features_select input:checkbox').each(function () {\n if(this.checked )\n beaker.selected_feature_list.push(this.value);\n });\n};\nvar getOperators = function() {\n beaker.allowed_operations = [];\n $('#lasso_operators_select input:checkbox').each(function () {\n if(this.checked )\n beaker.allowed_operations.push(this.value);\n });\n}; \n\nvar toggle_settings = function(){\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n if(e.style.display == 'block'){\n e.style.display = 'none';\n b.style.display = 'none';\n }\n else{\n e.style.display = 'block';\n b.style.display = 'inline';\n }\n};\nbeaker.view_result = function(result_link) {\n// beaker.evaluate(\"lasso_viewer_result\").then(function(x) {\n $(\"#lasso_result_button\").attr(\"href\", result_link);\n// }); \n $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");\n}\n\n\n\n\n\n\n\n</script>\n<style type=\"text/css\">\n .lasso_instructions{\n font-size: 15px;\n } \n</style>\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-motivation-modal\">\n Background\n</button> \n\n<!-- Modal -->\n<div class=\"modal fade\" id=\"lasso-motivation-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-motivation-modal-label\">\n <div class=\"modal-dialog modal-lg\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-motivation-modal-label\">Background</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n <p>We present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material. We apply a newly developed method: Sure Independence Screening + L0-regularization (SIS+L0), that allows to find an optimal descriptor in a huge feature space containing billions of features §.\nThe method is described in:\n</p><div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\nR. Ouyang, E. Ahmetcik, L. M. Ghiringhelli, and M. Scheffler: <span style=\"font-style: italic;\">Descriptor identification for material properties via compressed sensing</span>, in preparation.\n</div>\n<p></p>\n \n<p>SIS+L0 works iteratively. In the first iteration, a number k of features is collected that have the largest correlation (scalar product) with the property vector. The feature with the largest correlation is simply the 1D descriptor. Next, a residual is constructed as the error made at the first iteration. A new set of k features is now selected as those having the largest correlation with the residual. The 2D descriptor is the pair of features that yield the smallest fitting error upon least square regression, among all possible pairs contained in the union of the sets selected in this and the first iteration. In each next iteration a new residual is constructed as the error made in the previous iteration, then a new set of k features is extracted as those that have largest correlation with each new residual. The nD descriptor is the n-tuple of features that yield the smallest fitting error upon least square regression, among all possible n-tuples contained in the union of the sets obtained in each new iteration and all the previous iterations. If k=1 the method collapses to the so-called orthogonal matching pursuit.\n</p>\n\n <p>The prediction of the ground-state structure for binary compounds from a simple descriptor has a notable history in materials science [1-7], where descriptors were designed by chemically/physically-inspired intuition. The tool presented here allows for the machine-learning-aided automatic discovery of a descriptor and a model for the prediction of the difference in energy between a selected pair of structures for 82 octet binary materials.</p>\n\n\n <p> By running the tutorial with the default setting, the (RS vs. ZB) results of the <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a> identified by the LASSO+L0 method can be recovered.</p>\n\n<p>§ The same task is also addressed in the tutorial <a href=\"http://analytics-toolkit.nomad-coe.eu/tutorial-LASSO_L0\">\"[Predicting energy differences between different crystal structures I: large feature space]\"</a>, where an alternative method, LASSO+L0, is used to find an optimal descriptor in a moderately large feature space. LASSO+L0 was introduced in [PRL 2015] and SIS+L0 was introduced more recently in order to cope with much larger and highly correlated feature spaces.</p>\n\n <p>References:</p>\n <ol>\n <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>\n <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>\n <li>J. John and A. N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>\n <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>\n <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>\n <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>\n <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>\n </ol>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-instructions-modal\">\n Instructions\n</button> \n\n<!-- Modal -->\n<div class=\"modal fade\" id=\"lasso-instructions-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-instructions-modal-label\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-instructions-modal-label\">Instructions</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n<p>In this example, you can run a compressed-sensing based algorithm for finding the optimal descriptor and model that predicts the difference in energy between crystal structures (here, zincblende vs. rocksalt, CsCl, NiAs or CrB structure). </p>\n\n<p>The descriptor is selected out of a large number of candidates constructed as functions of basic input features, the primary features. </p>\n\n<p>By clicking <b>Settings</b> you can select the structure pair of interest (either RS/ZB, CsCl/ZB, NiAs/ZB or CrB/ZB), the primary features as well as which kind of unary and binary operations are allowed during feature space construction from the checklist below. Moreover the dimension of the output energies (kcal/mol or eV) and the following three parameters of the SIS+L0 algorithm can be specified: \n </p><ul>\n <li>Number of iterations for the construction for the feature space: How often the selected operations are applied to build the feature space. At each step the opreations are applied on all features created until the current step. </li>\n <li>Maximum dimension of optimal descriptor: Number of SIS+SA iterations.</li>\n <li>Number of collected features per SIS iteration.</li>\n </ul> \n \n<p></p>\n \n \n<p> After the preferred settings have been adjusted, click <b>RUN</b> for performing the calculations (loading the values of the primary features, creation of the feature space, and optimization via SIS+L0). </p>\n\nDuring the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: \n <ul>\n <li> the solution (machine-learned descriptor, model, and its performance in terms of training error) is printed out underneath starting from the one-dimensional solution to the selected maximum dimensionality and</li>\n<li> the “View interactive 2D scatter plot” button unlocks. By clicking this button, the scatter plot with the two-dimensional descriptor appears in a separate tab. If a descriptor dimensionality greater than two was selected, the scatter plot displays the two-dimensional descriptor.</li>\n</ul>\n<p>Note: the scatter plot remains active even if another run is performed, which enables the output of several sets of input parameters to be compared.</p>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n<!-- Button trigger modal \n<button type=\"button\" class=\"btn btn-default\" onclick='toggle_settings()'>\n Settings\n</button> -->\n\n<a target=\"_blank\" href=\"https://nomad-forum.rz-berlin.mpg.de/\" class=\"btn btn-primary\"> Tell us what you think</a>\n\n"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#modal-credits\" style=\"font-size:larger;\">Background</button>\n<div style=\"display: none;\" class=\"modal fade\" id=\"modal-credits\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-credits\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h1 class=\"modal-title\" id=\"modal-credits-label\">Background</h1>\n <hr>\n </div>\n <div class=\"modal-body modal-credits-body\">\n\n <p>We present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material. We apply a newly developed method: Sure Independence Screening + L0-regularization (SIS+L0), that allows to find an optimal descriptor in a huge feature space containing billions of features §.\nThe method is described in:\n</p><div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\nR. Ouyang, E. Ahmetcik, L. M. Ghiringhelli, and M. Scheffler: <span style=\"font-style: italic;\">Descriptor identification for material properties via compressed sensing</span>, in preparation.\n</div>\n<p></p>\n \n<p>SIS+L0 works iteratively. In the first iteration, a number k of features is collected that have the largest correlation (scalar product) with the property vector. The feature with the largest correlation is simply the 1D descriptor. Next, a residual is constructed as the error made at the first iteration. A new set of k features is now selected as those having the largest correlation with the residual. The 2D descriptor is the pair of features that yield the smallest fitting error upon least square regression, among all possible pairs contained in the union of the sets selected in this and the first iteration. In each next iteration a new residual is constructed as the error made in the previous iteration, then a new set of k features is extracted as those that have largest correlation with each new residual. The nD descriptor is the n-tuple of features that yield the smallest fitting error upon least square regression, among all possible n-tuples contained in the union of the sets obtained in each new iteration and all the previous iterations. If k=1 the method collapses to the so-called orthogonal matching pursuit.\n</p>\n\n <p>The prediction of the ground-state structure for binary compounds from a simple descriptor has a notable history in materials science [1-7], where descriptors were designed by chemically/physically-inspired intuition. The tool presented here allows for the machine-learning-aided automatic discovery of a descriptor and a model for the prediction of the difference in energy between a selected pair of structures for 82 octet binary materials.</p>\n\n\n <p> By running the tutorial with the default setting, the (RS vs. ZB) results of the <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a> identified by the LASSO+L0 method can be recovered.</p>\n\n<p>§ The same task is also addressed in the tutorial <a href=\"http://analytics-toolkit.nomad-coe.eu/tutorial-LASSO_L0\">\"[Predicting energy differences between different crystal structures I: large feature space]\"</a>, where an alternative method, LASSO+L0, is used to find an optimal descriptor in a moderately large feature space. LASSO+L0 was introduced in [<a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a>] and SIS+L0 was introduced more recently in order to cope with much larger and highly correlated feature spaces.</p>\n\n <p>References:</p>\n <ol>\n <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>\n <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>\n <li>J. John and A. N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>\n <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>\n <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>\n <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>\n <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>\n </ol> \n \n \n \n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\" style=\"font-size:larger;\">Close</button>\n </div>\n </div>\n </div>\n</div>\n\n\n<button type=\"button\" class=\"btn btn-info\" data-toggle=\"modal\" data-target=\"#modal-instructions\" style=\"font-size:larger;\">Read usage instructions</button>\n<div class=\"modal fade\" id=\"modal-instructions\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-instructions\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h1 class=\"modal-title\" id=\"modal-instructions-label\">Instructions</h1>\n <hr>\n </div>\n <div class=\"modal-body modal-instruction-body\">\n \n <p>In this example, you can run a compressed-sensing based algorithm for finding the optimal descriptor and model that predicts the difference in energy between crystal structures (here, zincblende vs. rocksalt, CsCl, NiAs or CrB structure). </p>\n\n<p>The descriptor is selected out of a large number of candidates constructed as functions of basic input features, the primary features. </p>\n\n<p>By clicking <b>Settings</b> you can select the structure pair of interest (either RS/ZB, CsCl/ZB, NiAs/ZB or CrB/ZB), the primary features as well as which kind of unary and binary operations are allowed during feature space construction from the checklist below. Moreover the dimension of the output energies (kcal/mol or eV) and the following three parameters of the SIS+L0 algorithm can be specified: \n </p><ul>\n <li>Number of iterations for the construction for the feature space: How often the selected operations are applied to build the feature space. At each step the operations are applied on all features created until the current step. </li>\n <li>Maximum dimension of optimal descriptor: Number of SIS iterations.</li>\n <li>Number of collected features per SIS iteration.</li>\n </ul> \n \n \n<p> After the preferred settings have been adjusted, click <b>RUN</b> for performing the calculations (loading the values of the primary features, creation of the feature space, and optimization via SIS+L0). </p>\n\nDuring the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: \n <ul>\n <li> the solution (machine-learned descriptor, model, and its performance in terms of training error) is printed out underneath starting from the one-dimensional solution to the selected maximum dimensionality and</li>\n<li> the “View interactive 2D scatter plot” button unlocks. By clicking this button, the scatter plot with the two-dimensional descriptor appears in a separate tab. If a descriptor dimensionality greater than two was selected, the scatter plot displays the two-dimensional descriptor.</li>\n</ul>\n<p>Note: the scatter plot remains active even if another run is performed, which enables the output of several sets of input parameters to be compared.</p>\n \n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\" style=\"font-size:larger;\">Close</button>\n </div>\n </div>\n </div>\n</div>\n\n<p></p>"
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+ "<h3 style=\"margin-top: 0;\">Feedback</h3>",
+ "<p>Want to give feedback?<br /><br />",
+ " <a target=\"_blank\" href=\"https://nomad-forum.rz-berlin.mpg.de\" class=\"btn btn-primary\" style=\"font-size: larger\">Send your feedback to the analytics-toolkit forum</a>",
+ "</p>",
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GitLab