-
Angelo Ziletti authoredAngelo Ziletti authored
LASSO_L0.bkr 79.28 KiB
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" <h3>Predicting energy differences between crystal structures I (large feature space): Octet-binary zincblende vs. rocksalt semiconductors</h3>",
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" Angelo Ziletti<sup> 1</sup>,",
" Ankit Kariryaa<sup>1</sup>, ",
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" <sup>1</sup> Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany <br>",
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"### Introduction ",
"",
"<div style=\"padding-top: 1em;\">",
"This tutorial shows how to find descriptive parameters (short formulas) that predict crystal structure, using the example of octet binary compounds that have either rocksalt (RS) or zincblende (ZB) structure. It is based on</div>",
"<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">",
"L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, M. Scheffler: <span style=\"font-style: italic;\">Big Data of Materials Science: Critical Role of the Descriptor</span>, Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\">[PDF]</a>",
"</div>",
"<div>",
"Click on \"Run\" below to reproduce results from this publication; click \"Background\" for an explanation of the approach; or, modify \"Settings\" to produce your own results.",
"</div>",
"<div style=\"padding-top: 2ex;\">",
"<span style=\"font-weight: bold;\">Idea: </span> Starting from simple physical quantities (\"building blocks\", here properties of the constituent free atoms such as orbital radii), thousands of candidate formulas are generated by applying arithmetic operations combining building blocks, for example forming sums and products of them. These candidate formulas constitute the so-called \"feature space\". Then, a sparse regression method is used to select only a few of these formulas that explain the data.",
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" <h1 class=\"modal-title\" id=\"modal-credits-label\">Background</h1>",
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"",
" <p> In this tutorial we present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material.</p>",
"",
" <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>",
"",
" <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>",
"",
" <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>",
"",
" <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>",
"",
" <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>",
" ",
" <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 <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>",
" <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>",
" <li>J. St. 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>",
" </div>",
" <div class=\"modal-footer\">",
" <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>",
"<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->",
" </div>",
" </div>",
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"</div>",
"",
"",
"<button type=\"button\" class=\"btn btn-info\" data-toggle=\"modal\" data-target=\"#modal-instructions\" style=\"font-size:larger;\">Read usage instructions</button>",
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" <div class=\"modal-dialog\" role=\"document\">",
" <div class=\"modal-content\">",
" <div class=\"modal-header\">",
" <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>",
" <h1 class=\"modal-title\" id=\"modal-instructions-label\">Instructions</h1>",
" <hr>",
" </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, rocksalt vs. zincblende). </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>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>",
"<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>",
"",
"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, 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>",
"</ul>",
"<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>",
" <div class=\"modal-footer\">",
" <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\" style=\"font-size:larger;\">Close</button>",
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"",
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"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#modal-credits\" style=\"font-size:larger;\">Background</button>\n<div style=\"display: none;\" class=\"modal fade\" id=\"modal-credits\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-credits\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h1 class=\"modal-title\" id=\"modal-credits-label\">Background</h1>\n <hr>\n </div>\n <div class=\"modal-body modal-credits-body\">\n\n <p> In this tutorial we present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material.</p>\n\n <p>In this example, we address only Rocksalt (RS) and Zincblende (ZB) crystal structures, that are the most common for the material class of octet binaries. Specifically, the tool predicts the difference in total energy between RS and ZB equilibrated structures (i.e., each structure is relaxed to its local minimum).</p>\n\n <p>The prediction of RS vs ZB structure from a simple descriptor has a notable history in materials science [1-7], where descriptors were designed by chemically/physically-inspired intuition. The tool presented here allows for the machine-learning-aided automatic discovery of a descriptor and a model for the prediction of the difference in energy between RS and ZB for 82 octet binary materials.</p>\n\n <p>The tool is based on Compressed-sensing (LASSO performed on a tailor-made feature space, followed by L0-regularized minimization, click <a href=\"https://gitlab.rzg.mpg.de/nomad-lab/public-wiki/wikis/analytics/LASSO_L0\" target=\"_blank\">here</a> for more info on the LASSO+L0 method), as introduced in: </p>\n\n <p> \"Big Data of Materials Science: Critical Role of the Descriptor\". L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, and M. Scheffler Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\"> (Click here for the free access pdf) </a></p>\n\n <p> By running the tutorial with the default setting, the results of the <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a> can be recovered. In particular, by clicking on “View interactive 2D plot”, an interactive structure-map (a chart where different structures are located in different regions of a low-dimensional representation, here two dimensional) will be opened in a new tab, similar to the following (an extended version of Fig. 2 in <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a>): </p>\n \n <img style=\"width:67%;height:67%\" src=\"https://gitlab.mpcdf.mpg.de/nomad-lab/public-wiki/uploads/eb33f1415db1b6d489cbbc3e6a899942/2016-08-02_ZB_RS3-2.png\">\n <br>\n <br>\n <p> In this map the octet binaries are located via the descriptor found by our LASSO+L0 approach. The descriptor is based purely on free-atom data, namely radii of the <i>s</i> and <i>p</i> valence orbitals (rs and rp) of the atomic species and their Ionizaiton Potential and Electron Affinity (IP and EA). Materials in the red (blue) region crystallize preferably in the zincblende (rocksalt) structure. The distance to the green line is proportional to the difference in energy between the two structures. In the interactive plot accessible at the end of the learning performed by the present tool, one can obtain information on the materials by hovering and clicking on the data points. </p>\n <p>References:</p>\n <ol>\n <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>\n <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>\n <li>J. St. John and A.N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>\n <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>\n <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>\n <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>\n <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>\n </ol>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n\n<button type=\"button\" class=\"btn btn-info\" data-toggle=\"modal\" data-target=\"#modal-instructions\" style=\"font-size:larger;\">Read usage instructions</button>\n<div style=\"display: none;\" class=\"modal fade\" id=\"modal-instructions\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-instructions\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h1 class=\"modal-title\" id=\"modal-instructions-label\">Instructions</h1>\n <hr>\n </div>\n <div class=\"modal-body modal-instruction-body\">\n \n<p>In this example, you can run a compressed-sensing based algorithm for finding the optimal descriptor (and model) that predicts the difference in energy between crystal structures (here, rocksalt vs. zincblende). </p>\n\n<p>The descriptor is selected out of a large number of candidates constructed as functions of basic input features, the primary features. </p>\n\n<p>You can select the primary features as well as which kind of unary and binary operations are allowed from the checklist below. You can also select the maximum dimensionality of the descriptor. </p>\n<p> After the wished features have been selected, click <b>RUN</b> to perform the calculations (loading the values of the primary features, creation of the feature space, and optimization via LASSO+L0). </p>\n\nDuring the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: \n <ul>\n <li> the solution (machine-learned descriptor, model, and its performance in terms of training error) is printed out underneath starting from the one-dimensional solution to the selected maximum dimensionality and</li>\n<li> the “View interactive 2D scatter plot” button unlocks; by clicking, the scatter plot with the two-dimensional descriptor appears in a separate tab. In case a dimensionality higher than 2 was selected for the descriptor, the plot displays the first two dimensions.</li>\n</ul>\n<p>Note: the plot stays active also after another run is performed, so that the output of several sets of input parameters can be compared in the viewer tabs.</p>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\" style=\"font-size:larger;\">Close</button>\n </div>\n </div>\n </div>\n</div>\n\n<p></p></div>"
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"<script>",
"var run_lasso = function() {",
" $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");",
" getFeatures();",
" getOperators();",
" getMaxDim();",
" getUnits();",
" beaker.evaluate(\"calc_cell\"); // evaluate cells with tag \"calc_cell\"",
" // view_result()",
"};",
"var reset_lasso = function(){",
" beaker.evaluate(\"lasso-settings-cell\");",
" var e = document.getElementById('lasso-hidden-settings-div');",
" var b = document.getElementById('lasso-hidden-settings-button');",
" e.style.display = 'block';",
" b.style.display = 'inline';",
"};",
"var getFeatures = function() {",
" beaker.selected_feature_list = [];",
" $('#lasso_features_select input:checkbox').each(function () {",
" if(this.checked )",
" beaker.selected_feature_list.push(this.value);",
" });",
"};",
"var getOperators = function() {",
" beaker.allowed_operations = [];",
" $('#lasso_operators_select input:checkbox').each(function () {",
" if(this.checked )",
" beaker.allowed_operations.push(this.value);",
" });",
"}; ",
"var getMaxDim = function() {",
" beaker.max_dim = $(\"#lasso_max_dim_selector\").val();",
"};",
" ",
"var getUnits = function() {",
" beaker.units = $(\"#units_select\").val();",
"};",
"var toggle_settings = function(){",
" var e = document.getElementById('lasso-hidden-settings-div');",
" var b = document.getElementById('lasso-hidden-settings-button');",
" if(e.style.display == 'block'){",
" e.style.display = 'none';",
" b.style.display = 'none';",
" }",
" else{",
" e.style.display = 'block';",
" b.style.display = 'inline';",
" }",
"};",
"beaker.view_result = function(result_link) {",
"// beaker.evaluate(\"lasso_viewer_result\").then(function(x) {",
" $(\"#lasso_result_button\").attr(\"href\", result_link);",
"// }); ",
" $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");",
"}",
"</script>"
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"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar run_lasso = function() {\n $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");\n getFeatures();\n getOperators();\n getMaxDim();\n getUnits();\n beaker.evaluate(\"calc_cell\"); // evaluate cells with tag \"calc_cell\"\n // view_result()\n};\nvar reset_lasso = function(){\n beaker.evaluate(\"lasso-settings-cell\");\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n e.style.display = 'block';\n b.style.display = 'inline';\n};\nvar getFeatures = function() {\n beaker.selected_feature_list = [];\n $('#lasso_features_select input:checkbox').each(function () {\n if(this.checked )\n beaker.selected_feature_list.push(this.value);\n });\n};\nvar getOperators = function() {\n beaker.allowed_operations = [];\n $('#lasso_operators_select input:checkbox').each(function () {\n if(this.checked )\n beaker.allowed_operations.push(this.value);\n });\n}; \nvar getMaxDim = function() {\n beaker.max_dim = $(\"#lasso_max_dim_selector\").val();\n};\n \nvar getUnits = function() {\n beaker.units = $(\"#units_select\").val();\n};\nvar toggle_settings = function(){\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n if(e.style.display == 'block'){\n e.style.display = 'none';\n b.style.display = 'none';\n }\n else{\n e.style.display = 'block';\n b.style.display = 'inline';\n }\n};\nbeaker.view_result = function(result_link) {\n// beaker.evaluate(\"lasso_viewer_result\").then(function(x) {\n $(\"#lasso_result_button\").attr(\"href\", result_link);\n// }); \n $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");\n}\n</script>"
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" <p class=\"lasso_selection_description\"><b>Primary features </b>",
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"pointer over the feature names to see a full description):</p>",
" <form id=\"lasso_features_select\">",
" <div class=\"lasso_form_group\">",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_ionization_potential\" CHECKED > <span title=\"Atomic ionization potential\"><i>IP</i> </span></label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_electron_affinity\" CHECKED > <span title=\"Atomic electron affinity\"> <i>EA</i></span></label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_homo\" CHECKED> <span title=\"Energy of highest occupied molecular orbital\"><i>E</i> <sub>HOMO</sub></span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_lumo\" CHECKED> <span title=\"Energy of lowest unoccupied molecular orbital\"> <i>E</i> <sub>LUMO</sub> </span> </label>",
" ",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_rs_max\" CHECKED > <span title=\"Radius at which the radial probability density of the valence s orbital is maximum\"> <i>r</i><sub>s</sub> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_rp_max\" CHECKED > <span title=\"Radius at which the radial probability density of the valence p orbital is maximum\"> <i>r</i><sub>p</sub> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_rd_max\" CHECKED > <span title=\"Radius at which the radial probability density of the valence d orbital is maximum\"> <i>r</i><sub>d</sub> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_number\" > <span title=\"Atomic number\"> <i>Z</i> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_number_valence_electrons\" > <span title=\"Number of valence electrons\"> <i>Z</i><sub>val</sub> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"period\" > <span title=\"Period (in the periodic table)\"> <i>n</i> <sub>period</sub> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_r_by_2_dimer\" > <span title=\"Bond length of the dimer\"> <i>d</i> <sub>dimer</sub> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_electronic_binding_energy_dimer\" > <span title=\"Binding energy of the dimer\"> <i>E</i> <sub>b</sub> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_homo_lumo_diff\" > <span title=\"HOMO-LUMO gap of the dimer\"> Δ<i>E</i><sub>HL</sub> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"r_sigma\" > <span title=\"John-Bloch's indicator1: |rp(A) + rs(A) - rp(B) -rs(B)| ",
" [Phys. Rev. Lett. 33. 1095 (1974)]\"> r<sub>σ</sub> </span> </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"r_pi\" > <span title=\"John-Bloch's indicator2: |rp(A) - rs(A)| +| rp(B) -rs(B)| ",
" [Phys. Rev. Lett. 33. 1095 (1974)]\"> r<sub>π</sub> </span> </label>",
" </div>",
" </form>",
" </div> <!-- End of row-->",
" <div class=\"row\"> <!-- Start of second row-->",
" <p class=\"lasso_selection_description\"><b>Allowed operations:</b> <br>",
" Given features x and y, apply these operations:</p>",
" <form id=\"lasso_operators_select\">",
" <div class=\"lasso_form_group\">",
" <label class =\"col-xs-4 col-md-4 col-lg-1\"> <input type=\"checkbox\" value=\"+\" CHECKED > x+y </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-1\"> <input type=\"checkbox\" value=\"-\" > x-y </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-1\"> <input type=\"checkbox\" value=\"|-|\" CHECKED > |x-y| </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-1\"> <input type=\"checkbox\" value=\"*\" > x · y </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-1\"> <input type=\"checkbox\" value=\"/\" CHECKED > x/y </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-1\"> <input type=\"checkbox\" value=\"^2\" CHECKED > x^2 </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"^3\" > x^3 </label>",
" <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"exp\" CHECKED > exp(x) </label>",
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" </form>",
" </div> <!-- End of row-->",
" <div class=\"row\"> <!-- Start of third row-->",
" <p class=\"lasso_selection_description\"><b>Optimal descriptor maximum dimension: </b> ",
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" <option value=\"3\" > 3D</option>",
" <option value=\"4\" > 4D</option>",
" <option value=\"5\" > 5D</option>",
" </select> </p>",
" </div><!-- End of row-->",
" <div class=\"row\"> <!-- Start of forth row-->",
" <p class=\"lasso_selection_description\"><b>Units of measurement: </b> ",
" <select id='units_select'>",
" <option value=\"eV_angstrom\" > [energy]=eV; [length]=angstrom</option>",
" <option value=\"J_m\" > [energy]=J; [length]=m</option>",
" <option value=\"kcal/mol_angstrom\" > [energy]=kcal/mol; [length]=angstrom</option>",
" </select> </p>",
" </div><!-- End of row-->",
" ",
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Rev. Lett. 33. 1095 (1974)]\"> r<sub>σ</sub> </span> </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"r_pi\" type=\"checkbox\"> <span title=\"John-Bloch's indicator2: |rp(A) - rs(A)| +| rp(B) -rs(B)| \n [Phys. Rev. Lett. 33. 1095 (1974)]\"> r<sub>π</sub> </span> </label>\n </div>\n </form>\n </div> <!-- End of row-->\n <div class=\"row\"> <!-- Start of second row-->\n <p class=\"lasso_selection_description\"><b>Allowed operations:</b> <br>\n Given features x and y, apply these operations:</p>\n <form id=\"lasso_operators_select\">\n <div class=\"lasso_form_group\">\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"+\" checked=\"\" type=\"checkbox\"> x+y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"-\" type=\"checkbox\"> x-y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"|-|\" checked=\"\" type=\"checkbox\"> |x-y| </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"*\" type=\"checkbox\"> x · y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"/\" checked=\"\" type=\"checkbox\"> x/y </label>\n <label class=\"col-xs-4 col-md-4 col-lg-1\"> <input value=\"^2\" checked=\"\" type=\"checkbox\"> x^2 </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"^3\" type=\"checkbox\"> x^3 </label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"exp\" checked=\"\" type=\"checkbox\"> exp(x) </label>\n </div>\n </form>\n </div> <!-- End of row-->\n <div class=\"row\"> <!-- Start of third row-->\n <p class=\"lasso_selection_description\"><b>Optimal descriptor maximum dimension: </b> \n <select id=\"lasso_max_dim_selector\">\n <option value=\"2\"> 2D</option>\n <option value=\"3\"> 3D</option>\n <option value=\"4\"> 4D</option>\n <option value=\"5\"> 5D</option>\n </select> </p>\n </div><!-- End of row-->\n <div class=\"row\"> <!-- Start of forth row-->\n <p class=\"lasso_selection_description\"><b>Units of measurement: </b> \n <select id=\"units_select\">\n <option value=\"eV_angstrom\"> [energy]=eV; [length]=angstrom</option>\n <option value=\"J_m\"> [energy]=J; [length]=m</option>\n <option value=\"kcal/mol_angstrom\"> [energy]=kcal/mol; [length]=angstrom</option>\n </select> </p>\n </div><!-- End of row-->\n \n</div>"
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" <button type=\"button\" class=\"btn btn-info\" style=\"font-size:larger; color: #ffffff; font-weight: bold;\" onclick='run_lasso()'>RUN</button>",
"",
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"",
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"dimensions of the optimized descriptor will be opened\"> ",
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"",
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"<p style=\"text-align: left; color: #aa3311; font-weight: 900; font-size: 20pt;\"> Note</p>",
"<p style=\"text-align: left; color: #aa3311; font-weight: 400; font-size: 16pt;\"> You will automatically generate atomic descriptors</i>, select an <i>optimal combination</i> among <i>thousands</i> of candidates, and produce a <i>fully-interactive plot</i> of your results. <br>",
"With the Default settings you will reproduce our publication cited above. <br>",
"Please understand that the whole procedure may take a few moments.</p>",
""
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"type": "BeakerDisplay",
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"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<p style=\"text-align: left; color: #aa3311; font-weight: 900; font-size: 20pt;\"> Note</p>\n<p style=\"text-align: left; color: #aa3311; font-weight: 400; font-size: 16pt;\"> You will automatically generate atomic descriptors, select an <i>optimal combination</i> among <i>thousands</i> of candidates, and produce a <i>fully-interactive plot</i> of your results. <br>\nWith the Default settings you will reproduce our publication cited above. <br>\nPlease understand that the whole procedure may take a few moments.</p>\n"
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"body": [
"from nomad_sim.wrappers import get_json_list, calc_descriptor ",
"from nomad_sim.wrappers import calc_model, calc_embedding, plot",
"from nomad_sim.utils_parsing import read_gdb_7k",
"from nomad_sim.utils_crystals import create_supercell",
"from nomad_sim.utils_crystals import create_vacancies",
"from nomad_sim.utils_crystals import random_displace_atoms",
"from nomad_sim.utils_crystals import substitute_atoms",
"from nomad_sim.descriptors import XrayDiffraction",
"from nomad_sim.utils_crystals import create_supercell",
"",
"# hack to change to local/Beaker mode in all files in the packages",
"# DEPRECATED",
"import __builtin__",
"__builtin__.isBeaker = True",
"",
"import hashlib",
"",
"import sys, os",
"import pandas as pd",
"import numpy as np",
"import json",
"",
"",
"# define paths",
"tmp_folder = '/home/beaker/.beaker/v1/web/tmp/'",
"control_file = '/home/beaker/.beaker/v1/web/tmp/control.json'",
"data_folder='/parsed/prod-022/FhiAimsParser2.0.0-2-gf9335c4/RWApItBGtGUDsfMVlHKqrjUQ4rShT/'",
"lookup_file = '/home/beaker/.beaker/v1/web/tmp/lookup.dat'",
"collection_path = '/home/beaker/test/nomad_sim/data_zcrs/ExtendedBinaries_Dimers_Atoms_new.json'",
"path_to_collection = '/home/beaker/test/nomad_sim/data_zcrs/ExtendedBinaries_Dimers_Atoms_new.json'",
"",
"",
"# define units",
"if beaker.units == 'eV_angstrom':",
" energy_unit = 'eV'",
" length_unit = 'angstrom'",
"elif beaker.units == 'J_m':",
" energy_unit = 'J'",
" length_unit = 'm'",
"elif beaker.units == 'kcal/mol_angstrom':",
" energy_unit = 'kcal/mol'",
" length_unit = 'angstrom'"
],
"hidden": true
},
"output": {
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
"shellId": "DAA2D8C538AB425A80B98CFCA72A766D",
"elapsedTime": 3892
},
"evaluatorReader": true,
"tags": "calc_cell",
"lineCount": 42
},
{
"id": "codeLARUgI",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [
"# pass only lowest energy structures to save time for demonstation purposes",
"# pass only lowest energy structures to save time for demonstation purposes",
"json_list = [",
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"",
""
],
"hidden": true
},
"output": {
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
"shellId": "DAA2D8C538AB425A80B98CFCA72A766D",
"elapsedTime": 471,
"height": 986
},
"evaluatorReader": true,
"lineCount": 87,
"tags": "calc_cell"
},
{
"id": "codeO2f3dY",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [
"# get the json_list",
"#json_list = get_json_list(method='folder', drop_duplicates=False, ",
"# data_folder=data_folder, tmp_folder=tmp_folder)",
"",
"operations_on_structure = [(create_supercell, {'replicas': [3, 3, 3]})]",
"op_list = np.zeros(len(json_list))",
"",
"kwargs = {'path_to_collection': path_to_collection,",
" 'feature_order_by': 'atomic_mulliken_electronegativity',",
" 'energy_unit': energy_unit,",
" 'length_unit': length_unit}",
"",
"dict_delta_e = {'SeZn': 4.2159179660687287e-20, 'InSb': 1.2506570609555687e-20, 'AgCl': -6.8568799955090609e-21, 'SZn': 4.4190167090138643e-20, 'BN': 2.7430550221687302e-19, 'GaSb': 2.4773702178144287e-20, 'BrRb': -2.6246943074069208e-20, 'BaTe': -6.0143598233041078e-20, 'BeSe': 7.9298202208796193e-20, 'MgS': -1.3890792272791661e-20, 'AsB': 1.4018696180162358e-19, 'AlAs': 3.416831556361176e-20, 'BP': 1.632978757533666e-19, 'TeZn': 3.9253535511316122e-20, 'MgSe': -8.8603255975880209e-21, 'ClLi': -6.149391549293258e-21, 'FK': -2.3456843288794608e-20, 'BrLi': -5.2465217764284492e-21, 'BSb': 9.3062289002001379e-20, 'ClRb': -2.5715504707788539e-20, 'GeSn': 1.3083912918128912e-20, 'CsI': -2.6017342042091155e-20, 'CaTe': -5.6149286826500206e-20, 'ClK': -2.6349506219210773e-20, 'Sn2': 2.7179163244033424e-21, 'BrCs': -2.4972695386489382e-20, 'CsF': -1.734569615637085e-20, 'BrCu': 2.442400384019518e-20, 'CaSe': -5.7806170659176063e-20, 'AgF': -2.4634695420313482e-20, 'MgTe': -7.3560522736479063e-22, 'FLi': -9.5310792412059186e-21, 'CuF': -2.7272687327072279e-21, 'FNa': -2.3357835066436331e-20, 'C2': 4.2114873809101575e-19, 'BaO': -1.4900011177054134e-20, 'AgBr': -4.8118839046830307e-21, 'MgO': -3.721451404088126e-20, 'FRb': -2.1724838814450727e-20, 'AlN': 1.1687730189874494e-20, 'Si2': 4.4727296163305501e-20, 'SiSn': 2.1646816357014748e-20, 'OSr': -3.5297012817210187e-20, 'ClNa': -2.1307665354820141e-20, 'AsIn': 2.14767895373523e-20, 'OZn': 1.633710321777262e-20, 'CGe': 1.3000748379902827e-19, 'CdO': -1.348413629348854e-20, 'InP': 2.8709930119109753e-20, 'SSr': -5.9029656118592692e-20, 'InN': 2.4628706405675984e-20, 'BaSe': -5.5025977545738119e-20, 'BrK': -2.6624325013472597e-20, 'BeTe': 7.5075740576200973e-20, 'CdS': 1.1643465692630618e-20, 'CdTe': 1.8351256432814928e-20, 'GeSi': 4.217091895225337e-20, 'GaP': 5.5876198809236085e-20, 'CdSe': 1.3389702567051265e-20, 'INa': -1.8399111846593045e-20, 'AlP': 3.5080994271602511e-20, 'BeO': 1.1084460139894976e-19, 'AsGa': 4.3944144343852047e-20, 'Ge2': 3.218012279776111e-20, 'SeSr': -6.0003270319365202e-20, 'CSi': 1.071894196156348e-19, 'BaS': -5.1231589897332471e-20, 'AgI': 5.9161045280535275e-21, 'GaN': 6.9445584247860156e-20, 'CaS': -5.9141658617526103e-20, 'AlSb': 2.5133142314028706e-20, 'IK': -2.6762621853286689e-20, 'ILi': -3.4704646494924847e-21, 'ClCs': -2.4088110334584613e-20, 'CaO': -4.2492775596486839e-20, 'CuI': 3.2792483878850995e-20, 'CSn': 7.2664795347721018e-20, 'BeS': 8.1122637897805144e-20, 'IRb': -2.6788624696652254e-20, 'BrNa': -2.0256115610545176e-20, 'SrTe': -6.0769715445352658e-20, 'ClCu': 2.5035406212316063e-20}",
"",
"derived_features = []",
"selected_feature_list = beaker.selected_feature_list",
"",
"if 'r_sigma' in selected_feature_list:",
" derived_features.append('r_sigma')",
" selected_feature_list.remove('r_sigma')",
" selected_feature_list.append('atomic_rs_max')",
" selected_feature_list.append('atomic_rp_max')",
" ",
"if 'r_pi' in selected_feature_list:",
" derived_features.append('r_pi')",
" selected_feature_list.remove('r_pi')",
" selected_feature_list.append('atomic_rs_max')",
" selected_feature_list.append('atomic_rp_max')",
" ",
"",
"descriptor = calc_descriptor(",
" desc_type='atomic_features',",
" selected_feature_list=selected_feature_list,",
" dict_delta_e=dict_delta_e,",
" op_list=op_list,",
" operations_on_structure=operations_on_structure,",
" json_list=json_list, tmp_folder=tmp_folder,",
" **kwargs)",
""
],
"hidden": true
},
"output": {
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
"shellId": "DAA2D8C538AB425A80B98CFCA72A766D",
"elapsedTime": 15139,
"height": 100
},
"evaluatorReader": true,
"lineCount": 39,
"tags": "calc_cell"
},
{
"id": "codeo2yP7p",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [
"calc_model(method='ext_lasso', desc_type='atomic_features',",
" max_dim=int(beaker.max_dim),",
" allowed_operations=beaker.allowed_operations,",
" derived_features=derived_features,",
" tmp_folder=tmp_folder, ",
" lookup_file=lookup_file, ",
" control_file=control_file,",
" energy_unit=energy_unit, length_unit=length_unit)",
""
],
"hidden": true
},
"output": {
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
"shellId": "DAA2D8C538AB425A80B98CFCA72A766D",
"elapsedTime": 7785,
"height": 78
},
"evaluatorReader": true,
"lineCount": 9,
"tags": "calc_cell"
},
{
"id": "code7NKF5Q",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [
"parameter_list = beaker.selected_feature_list",
"parameter_list.append(beaker.allowed_operations)",
"name_html_page = hashlib.sha224(str(parameter_list)).hexdigest()[:16]",
"",
"",
"json_list, frame_list, x_list, y_list, target_list, target_pred_list = get_json_list(method='file', data_folder=data_folder,",
" path_to_file=lookup_file, drop_duplicates=False, displace_duplicates=True, predicted_value=True)",
"beaker.viewer_result = name_html_page",
"",
"",
"plot_result = plot(name=name_html_page, json_list=json_list, frames='list', frame_list=frame_list, ",
" file_format='NOMAD', clustering_x_list=x_list, clustering_y_list=y_list, target_list=target_list,",
" target_unit=energy_unit, energy_unit=energy_unit,",
" legend_title='Reference E(RS)-E(ZB)', target_name='E(RS)-E(ZB)',",
" target_pred_list=target_pred_list,",
" plot_title='LASSO+L0 structure map',",
" clustering_point_size=12, tmp_folder=tmp_folder, control_file=control_file,",
" op_list=op_list, operations_on_structure=operations_on_structure)"
],
"hidden": true
},
"output": {
"state": {},
"selectedType": "Results",
"height": 78,
"pluginName": "IPython",
"shellId": "DAA2D8C538AB425A80B98CFCA72A766D",
"elapsedTime": 7592
},
"evaluatorReader": true,
"lineCount": 18,
"tags": "calc_cell"
},
{
"id": "codedHnZ58",
"type": "code",
"evaluator": "HTML",
"input": {
"body": [
"<h3 style=\"margin-top: 0;\">Feedback</h3>",
"<p>Want to give feedback?<br /><br />",
" <a target=\"_blank\" href=\"https://nomad-forum.rz-berlin.mpg.de\" class=\"btn btn-primary\" style=\"font-size: larger\">Send your feedback to the analytics-toolkit forum</a>",
"</p>",
"<p>Your comments are invaluable in helping us to provide a user-friendly experience for all!<p>"
],
"hidden": true
},
"output": {
"state": {},
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<h3 style=\"margin-top: 0;\">Feedback</h3>\n<p>Want to give feedback?<br><br>\n <a target=\"_blank\" href=\"https://nomad-forum.rz-berlin.mpg.de\" class=\"btn btn-primary\" style=\"font-size: larger\">Send your feedback to the analytics-toolkit forum</a>\n</p>\n<p>Your comments are invaluable in helping us to provide a user-friendly experience for all!</p><p></p>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 245
},
"evaluatorReader": true,
"lineCount": 5
},
{
"id": "codeoorGVf",
"type": "code",
"evaluator": "JavaScript",
"input": {
"body": [
"var result_link = '/user/tmp/' + beaker.viewer_result + '.html';",
"beaker.view_result(result_link);"
],
"hidden": true
},
"output": {
"state": {},
"selectedType": "BeakerDisplay",
"height": 78,
"pluginName": "JavaScript",
"elapsedTime": 60
},
"evaluatorReader": true,
"lineCount": 2,
"tags": "calc_cell"
}
],
"namespace": {
"selected_feature_list": [
"atomic_ionization_potential",
"atomic_electron_affinity",
"atomic_homo",
"atomic_lumo",
"atomic_rs_max",
"atomic_rp_max",
"atomic_rd_max"
],
"allowed_operations": [
"+",
"|-|",
"/",
"^2",
"exp"
],
"max_dim": "2",
"ncomb": "1",
"n_sis": "50",
"n_comb": "1",
"viewer_result": "d934cec27120a732",
"units": "eV_angstrom"
},
"locked": true
}