Commit f510c057 authored by Emre Ahmetcik's avatar Emre Ahmetcik
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Edited background/instructions text

parent c421c4fc
......@@ -97,7 +97,7 @@
" <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 the metal-insulator classification of of elements and binary systems</h3>",
" <h3>Predicting the metal-insulator classification of elements and binary systems</h3>",
" <p class=\"nomad--description\">",
" created by:",
" Emre Ahmetcik<sup>1</sup>,",
......@@ -128,7 +128,7 @@
"result": {
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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 the metal-insulator classification of of elements and binary systems</h3>\n <p class=\"nomad--description\">\n created by:\n Emre Ahmetcik<sup>1</sup>,\n Angelo Ziletti<sup> 1</sup>,\n Runhai Ouyang<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: October 20, 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"
"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 the metal-insulator classification of elements and binary systems</h3>\n <p class=\"nomad--description\">\n created by:\n Emre Ahmetcik<sup>1</sup>,\n Angelo Ziletti<sup> 1</sup>,\n Runhai Ouyang<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: October 20, 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"
},
"elapsedTime": 0
},
......@@ -166,33 +166,23 @@
" </div>",
" <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 and sparsifying operator (SISSO), that allows to find an optimal descriptor in a huge feature space containing billions of features &#167;. In this tutorial an $\\ell_0$-optimization is used as the sparsifying operator.",
"The method is described in:",
" <p>We present a tool for predicting the metal-insulator classification of elements and binary systems, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the elemental/binary materials as well as a unit cell dependentent packing parameter (the normalized ratio between the volume of spherical atoms and the unit cell). The data is extracted from the <a href=\" http://materials.springer.com/\">SpringerMaterials</a> data base. We apply a newly developed method: sure independence screening and sparsifying operator (SISSO), that allows to find an optimal descriptor in a huge feature space containing billions of features. In this tutorial an $\\ell_0$-optimization is used as the sparsifying operator. 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, S. Curtarolo, E. Ahmetcik, L. M. Ghiringhelli, and M. Scheffler: <span style=\"font-style: italic;\">SISSO: a compressed-sensing method for systematically identifying efficient physical models of materials properties, </span> <a href=\" https://arxiv.org/abs/1710.03319\">https://arxiv.org/abs/1710.03319</a> (2017). <br>",
"</div>",
"</p>",
" ",
"<p>SISSO($\\ell_0$) 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.",
"<p>SISSO($\\ell_0$) for classification seeks in an iterative approach for that desriptor in whose space the overlap between class domains is minimal, where a class domain is represented by the convex hull of the training data. In the first iteration, a number $k$ of features is collected which seperate the convex hulls best. The feature with the lowest domain overlap is simply the 1D descriptor. In the next iteration, a new set of $k$ features is selected, now as those seperating the unclassified data from the first iteration best. The 2D descriptor is the pair of features that yield the smallest overlap region, among all possible pairs contained in the union of the sets selected in this and the first iteration. In each next iteration new set of $k$ features is extracted as those that separate the unclassified data from the previous step best. The $n$D descriptor is the $n$-tuple of features that yield the smallest convex hull overlap regions, among all possible $n$-tuples contained in the union of the sets obtained in each new iteration and all the previous iterations. ",
"</p>",
"",
" <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>",
"",
"",
" <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+$\\ell_0$ method can be recovered.</p>",
" <p>Note that this tutorial is constrained to seek for 2D descriptors only. SISSO can also be applied to regressions problems as i.e. described in the <a href=\" https://analytics-toolkit.nomad-coe.eu/notebook-edit/data/shared/tutorials/sis_cscl.bkr\"> SISSO tutorial for the prediction of the energy difference between crystal structures</a>. <br>",
"The SISSO approach was developed following a compressed-sensing based methodology to solve materials science problems as introduced in",
" <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>",
"in which the LASSO+$\\ell_0$ method was proposed.",
" </p>",
"",
"<p>&#167; 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+$\\ell_0$, is used to find an optimal descriptor in a moderately large feature space. LASSO+$\\ell_0$ was introduced in [<a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a>] and SISSO($\\ell_0$) was introduced more recently in order to cope with much larger and highly correlated feature spaces.</p>",
"",
" <p>References:</p>",
" <ol>",
" <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>",
" <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>",
" <li>J. John and A. N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>",
" <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>",
" <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> ",
" ",
" ",
" ",
......@@ -214,24 +204,21 @@
" </div>",
" <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>In this example, you can run the SISSO($\\ell_0$) algorithm for finding the optimal descriptor that classifies elements and binary systems into metals and non metals. The descriptor is selected out of a large number of candidates constructed as functions of basic input features, the primary features. </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 SISSO($\\ell_0$) algorithm can be specified: ",
"<p>In the settings you can select 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 following two parameters of the SISSO($\\ell_0$) 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 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> 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 SISSO($\\ell_0$)). </p>",
"<p> After the preferred settings have been adjusted, click <b>RUN</b> for performing the calculations (creation of the feature space and optimization via SISSO($\\ell_0$)). </p>",
"",
"During the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: ",
" <ul>",
" <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>",
"<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>",
" <li> the solution (identified 2D descriptor, and the number of data points in the overlap region) is printed out.</li>",
"<li> the “View interactive 2D scatter plot” button unlocks. By clicking this button, the scatter plot with the 2D descriptor appears in a separate tab.</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>",
" ",
......@@ -1015,15 +1002,15 @@
" if calc_time < 5.:",
" print \"INFO: Estimated calculation time: below 5 min\" ",
" else:",
" print \"WARNING: Estimated calculation time: %s min\" % calc_time"
" print \"WARNING: Estimated calculation time: %s min\" % int(calc_time)"
],
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"elapsedTime": 1095,
"selectedType": "Hidden"
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"evaluatorReader": true,
......
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