From cf9d8689ecc40565f295f9980218c87c925e8017 Mon Sep 17 00:00:00 2001
From: Angelo Ziletti <ziletti@fhi-berlin.mpg.de>
Date: Mon, 30 Jan 2017 16:46:28 +0100
Subject: [PATCH] Fix wrong json_list (rs/zb sometimes inverted).
---
beaker-notebooks/Embedding.bkr | 215 ++++++++++++++++----------------
beaker-notebooks/LASSO_L0.bkr | 217 ++++++++++++++++-----------------
2 files changed, 216 insertions(+), 216 deletions(-)
diff --git a/beaker-notebooks/Embedding.bkr b/beaker-notebooks/Embedding.bkr
index 46f3e43..72231b6 100644
--- a/beaker-notebooks/Embedding.bkr
+++ b/beaker-notebooks/Embedding.bkr
@@ -55,7 +55,7 @@
"<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 2017-01-23]</span></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 2017-01-30]</span></p>",
" ",
"<div style=\"padding-top: 1em;\">"
],
@@ -66,7 +66,7 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
- "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 Visualizing material-similarity:</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 2017-01-23]</span></p>\n \n<div style=\"padding-top: 1em;\"></div>"
+ "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 Visualizing material-similarity:</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 2017-01-30]</span></p>\n \n<div style=\"padding-top: 1em;\"></div>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
@@ -109,7 +109,7 @@
" <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, ",
" 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>",
"",
- " <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 is independent of the embedding method used, therefore the labeling is an <i>a posteriori</i>",
+ " <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>",
" 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>",
" ",
"<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>",
@@ -141,7 +141,7 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
- "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 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 is 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<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>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
@@ -330,7 +330,7 @@
" ",
" <br>",
" <div class=\"row\"> <!-- Start of row-->",
- " <p class=\"lasso_selection_description\"><b>Units of measure: </b> ",
+ " <p class=\"lasso_selection_description\"><b>Unit of measures: </b> ",
" <select id='units_select'>",
" <option value=\"eV_angstrom\" > [energy]=eV; [length]=angstrom</option>",
" <option value=\"J_m\" > [energy]=J; [length]=m</option>",
@@ -378,9 +378,9 @@
"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 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 measure: </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"
+ "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>Unit of measures: </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"
},
- "height": 545
+ "height": 530
},
"evaluatorReader": true,
"lineCount": 137
@@ -437,12 +437,12 @@
"hidden": true
},
"output": {
- "selectedType": "BeakerDisplay",
+ "selectedType": "Results",
"state": {},
"pluginName": "IPython",
- "shellId": "D7DE8A033E0A49EB8A87DC9597DD4326",
+ "shellId": "3A80290908F64F10A4B82404FBACE0BA",
"height": 103,
- "elapsedTime": 1101
+ "elapsedTime": 3244
},
"evaluatorReader": true,
"lineCount": 42,
@@ -460,88 +460,88 @@
"",
"# pass only lowest energy structures to save time for demonstation purposes",
"json_list = [",
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+ "elapsedTime": 48,
"height": 51
},
"evaluatorReader": true,
@@ -823,7 +823,10 @@
"namespace": {
"selected_feature_list": [
"atomic_ionization_potential",
- "atomic_rs_max"
+ "atomic_electron_affinity",
+ "atomic_rs_max",
+ "atomic_rp_max",
+ "atomic_rd_max"
],
"allowed_operations": [
"|-|",
@@ -838,9 +841,9 @@
11
],
"runInfo": "running Lasso",
- "viewer_result": "68013492a0f6f33e",
+ "viewer_result": "639febbfc57f4bc6",
"embed_method": "pca",
- "standardize": "False",
+ "standardize": "True",
"units": "eV_angstrom"
},
"locked": true
diff --git a/beaker-notebooks/LASSO_L0.bkr b/beaker-notebooks/LASSO_L0.bkr
index 77bf5c3..37b9813 100644
--- a/beaker-notebooks/LASSO_L0.bkr
+++ b/beaker-notebooks/LASSO_L0.bkr
@@ -42,7 +42,7 @@
"<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 2017-01-27]</span></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 2017-01-30]</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>",
@@ -53,7 +53,7 @@
"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. Then, a sparse regression method is used to select only a few of these formulas that explain the data.",
+ "<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>"
],
"hidden": true
@@ -63,11 +63,11 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
- "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 2017-01-27]</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. 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<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 2017-01-30]</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>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
- "height": 314
+ "height": 317
},
"evaluatorReader": true,
"lineCount": 16
@@ -169,7 +169,7 @@
" <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\">",
" <br/>",
" <br/>",
- " <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 s and p 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>",
+ " <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>",
" <p>References:</p>",
" <ol>",
" <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>",
@@ -240,11 +240,11 @@
"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>\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 s and p 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<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" onclick=\"toggle_settings()\">\n Settings\n</button> \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<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 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\">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<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" onclick=\"toggle_settings()\">\n Settings\n</button> \n\n\n<a target=\"_blank\" href=\"http://forum.analytics-toolkit.nomad-coe.eu/\" class=\"btn btn-primary\"> Tell us what you think</a>"
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@@ -317,7 +317,7 @@
" </select> </p>",
" </div><!-- End of row-->",
" <div class=\"row\"> <!-- Start of forth row-->",
- " <p class=\"lasso_selection_description\"><b>Units of measure: </b> ",
+ " <p class=\"lasso_selection_description\"><b>Unit of measures: </b> ",
" <select id='units_select'>",
" <option value=\"eV_angstrom\" > [energy]=eV; [length]=angstrom</option>",
" <option value=\"J_m\" > [energy]=J; [length]=m</option>",
@@ -340,11 +340,11 @@
"result": {
"type": "BeakerDisplay",
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- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n #lasso-hidden-settings-div{\n display:none;\n } \n #lasso-hidden-settings-button{\n display:none;\n } \n</style>\n<div style=\"display: none;\" 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 measure: </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<!-- <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 LASSO+L0</button> -->\n<!-- <button class=\"btn btn-default\" onclick='reset_lasso()'>RESET</button> -->\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\" id=\"lasso_result_button\" >View interactive 2D scatter plot</a> </label> -->\n</div>"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n #lasso-hidden-settings-div{\n display:none;\n } \n #lasso-hidden-settings-button{\n display:none;\n } \n</style>\n<div style=\"display: none;\" 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>Unit of measures: </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<!-- <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 LASSO+L0</button> -->\n<!-- <button class=\"btn btn-default\" onclick='reset_lasso()'>RESET</button> -->\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\" id=\"lasso_result_button\" >View interactive 2D scatter plot</a> </label> -->\n</div>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
- "height": 50
+ "height": 51
},
"evaluatorReader": true,
"lineCount": 77,
@@ -378,7 +378,7 @@
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
- "height": 86
+ "height": 87
},
"evaluatorReader": true,
"lineCount": 10
@@ -436,10 +436,10 @@
},
"output": {
"state": {},
- "selectedType": "Hidden",
+ "selectedType": "Results",
"pluginName": "IPython",
- "shellId": "F049749CBC474DC8A7783EA52ABB0B3A",
- "elapsedTime": 748
+ "shellId": "D5EE44586871408C813A90FDC3539856",
+ "elapsedTime": 1721
},
"evaluatorReader": true,
"tags": "calc_cell",
@@ -453,88 +453,88 @@
"body": [
"# pass only lowest energy structures to save time for demonstation purposes",
"json_list = [",
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- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PzZe8HJ1RoiT6LBluiHmTN9IDP6vE.json',",
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- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PezFp7D_Pzi-KwwYE9WlnFWtpTP_2.json',",
- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PlQ7NfvecVk8-o2I_Fbz0hNtkAJAw.json',",
- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PWBxopsGbXEANMPDUxcMi-PzKvqxH.json',",
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- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PVH2AkTXt2QDVEfJdFkGPAMk1_dQO.json',",
- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PLoQIJtvhgUQcXFhb0k_6mWOPV9NI.json',",
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- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PN0pxdAiZpbbUV2jORc4LSy5MaYWe.json',",
- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PGex81N1PxLHkRkSGopRqqLQ4tSkp.json',",
- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PWNJV2eK0tIrw_AEg-EpXTggLH88h.json',",
- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PbkJ-LOXCmwltwIWDXwnHWXpySRVi.json',",
- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PB-GqTDr-DZ-j4OKjRNJEp1hnOGvG.json',",
- "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pb4jnAWzhAL1kkV-J0QHJTsWUtBCj.json',",
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""
],
"hidden": true
@@ -543,8 +543,8 @@
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
- "shellId": "F049749CBC474DC8A7783EA52ABB0B3A",
- "elapsedTime": 429,
+ "shellId": "D5EE44586871408C813A90FDC3539856",
+ "elapsedTime": 391,
"height": 986
},
"evaluatorReader": true,
@@ -603,8 +603,8 @@
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
- "shellId": "F049749CBC474DC8A7783EA52ABB0B3A",
- "elapsedTime": 13708,
+ "shellId": "D5EE44586871408C813A90FDC3539856",
+ "elapsedTime": 13980,
"height": 100
},
"evaluatorReader": true,
@@ -633,8 +633,8 @@
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
- "shellId": "F049749CBC474DC8A7783EA52ABB0B3A",
- "elapsedTime": 7393,
+ "shellId": "D5EE44586871408C813A90FDC3539856",
+ "elapsedTime": 6690,
"height": 78
},
"evaluatorReader": true,
@@ -673,8 +673,8 @@
"selectedType": "Results",
"height": 78,
"pluginName": "IPython",
- "shellId": "F049749CBC474DC8A7783EA52ABB0B3A",
- "elapsedTime": 5078
+ "shellId": "D5EE44586871408C813A90FDC3539856",
+ "elapsedTime": 6001
},
"evaluatorReader": true,
"lineCount": 18,
@@ -696,7 +696,7 @@
"selectedType": "BeakerDisplay",
"height": 78,
"pluginName": "JavaScript",
- "elapsedTime": 32
+ "elapsedTime": 56
},
"evaluatorReader": true,
"lineCount": 2,
@@ -707,8 +707,6 @@
"selected_feature_list": [
"atomic_ionization_potential",
"atomic_electron_affinity",
- "atomic_homo",
- "atomic_lumo",
"atomic_rs_max",
"atomic_rp_max",
"atomic_rd_max"
@@ -724,8 +722,7 @@
"ncomb": "1",
"n_sis": "50",
"n_comb": "1",
- "viewer_result": "d934cec27120a732",
+ "viewer_result": "6edb65b9776deeb2",
"units": "eV_angstrom"
- },
- "locked": true
+ }
}
--
GitLab