-
Angelo Ziletti authoredAngelo Ziletti authored
clusterX.bkr 65.48 KiB
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" <h2> <img id=\"nomad\" src=\"https://nomad-coe.eu/uploads/nomad/images/NOMAD_Logo2.png\" height=\"100\" alt=\"NOMAD Logo\"> NOMAD Analytics Toolkit ",
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" <h3> Predicting ground-states of binary alloys through cluster expansion: Example of a SiGe binary alloy </h3>",
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" created by:",
" S. Rigamonti<sup> 1</sup>,",
" M. Troppenz<sup>1</sup>, ",
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" <sup>1</sup> Humboldt-Universität zu Berlin, Zum Großen Windkanal 6, D-12489 Berlin, Germany <br>",
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" This tutorial shows how to predict the ground-state configurations of a binary alloy, starting from a set of <i>ab-initio</i> calculations for random distributions of the substituent species. This is achieved through a cluster expansion (CE) of the energy of mixing of the alloy and a configurational sampling. The system studied in this tutorial is the alloy Si<sub>1-x</sub>Ge<sub>x</sub>. The user can specify the parameters determining the quality of the CE and the configurational sampling. The tasks are performed with the cluster expansion package CELL - Cluster Expansion for large parent ceLLs.",
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" <span style=\"font-weight: bold;\">Idea: </span> Starting from a set of arbitrary structures, with different compositions and various arrangements of the substitutional species (Ge in this case), the most stable arrangements of the Ge atoms in the crystal are predicted. ",
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"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\n var clearyy = function() { \n var d = document.getElementsByClassName('clearonload');\n for (var i=0; i<d.length; i++){\n d[i].style.display = 'none';\n }; \n beaker.toggle_outs=1;\n beaker.evaluate(\"calc_cell4\");\n beaker.evaluate(\"calc_cell6\");\n };\n \n beaker.evaluate(\"calc_imports\");\n clearyy();\n \n \n</script>\n\n<div style=\"padding-top: 1em;\">\n This tutorial shows how to predict the ground-state configurations of a binary alloy, starting from a set of <i>ab-initio</i> calculations for random distributions of the substituent species. This is achieved through a cluster expansion (CE) of the energy of mixing of the alloy and a configurational sampling. The system studied in this tutorial is the alloy Si<sub>1-x</sub>Ge<sub>x</sub>. The user can specify the parameters determining the quality of the CE and the configurational sampling. The tasks are performed with the cluster expansion package CELL - Cluster Expansion for large parent ceLLs.\n</div>\n<!--div>\n Click on \"Run\" below to reproduce results from this publication; Click on\"Background\" for an explanation of the cluster expansion technique.\n </div-->\n<div style=\"padding-top: 1em;\">\n <div style=\"padding-top: 2ex;\">\n <span style=\"font-weight: bold;\">Idea: </span> Starting from a set of arbitrary structures, with different compositions and various arrangements of the substitutional species (Ge in this case), the most stable arrangements of the Ge atoms in the crystal are predicted. \n </div>\n</div>\n"
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"\t<p>Substitutional alloys, such as, <i>e.g.</i>, Si<sub>1-x</sub>Ge<sub>x</sub>, present a multitude of atomic arrangements, or configurations. Thus, it is impossible to perform numerically costly <i>ab-initio</i> calculations for every of them. The cluster-expansion (CE) technique allows for building numerically efficient models to predict the energy E of the alloy by exploiting the unique dependence of E on the configuration (<i>i.e.</i> the specific arrangement of Ge atoms in the crystal). It relies on a set of <i>ab-initio</i> calculations obtained by density-functional theory and has been applied to describe stable phases of bulk and surface alloys</p>",
"",
"\t<p>With this technique, the energy can be parametrized in terms of clusters (a set of crystal sites). The energy contribution of every of them is termed effective-cluster interaction (ECI). Thus, for an arbitrary configuration of the alloy, the clusters formed by the substitutional species are found and the sum of the respective ECIs gives a prediction of the energy for the given configuration. Each cluster is defined by a specific number and arrangement of crystal sites (empty cluster, 1-point clusters, ..., n-point clusters). </p>",
"\t<p>",
"\t Through a cross-validation procedure, the optimal set of clusters to represent the energy of the alloy are found. This set is then used to build a model which allows quick and accurate predictions of the energy for arbitrary configurations. Using this model, millions of structures can be sampled, and thus the ground state configurations can be found. The knowledge of the stable structures is of fundamental importance for the understanding of the physical properties of the alloys. ",
"\t</p>",
"\t<p>",
"\t The tool makes use of the python package <tt>CELL</tt> (Cluster Expansion for large parent ceLLs). An application of <tt>CELL</tt> for the prediction of stable phases of complex thermoelectric alloys, can be found in:</p>",
"\t <div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">",
"\t M. Troppenz, S. Rigamonti, and C. Draxl: <span style=\"font-style: italic;\">Predicting Ground-State Configurations and Electronic Properties of the",
"\t Thermoelectric Clathrates Ba<sub>8</sub>Al<sub>x</sub>Si<sub>46-x</sub> and Sr<sub>8</sub>Al<sub>x</sub>Si<sub>46-x</sub></span>, accepted for publication in Chemistry of Materials (2017).",
"\t </div>",
"\t",
"\t<p><tt>CELL</tt> uses the <b>corrdump</b> utility of ATAT for the generation of clusters and the construction of the correlation matrix (A. van de Walle, CALPHAD: Comput. Coupling Phase Diagrams Thermochem. 33, 266-278 (2009))</p>",
"",
"\t<p>References:</p>",
" <ol>",
" <li>J. M. Sanchez, F. Ducastelle, and D. Gratias, Phys. A 128, 334-350 (1984).</li>",
" <li>A. van de Walle and G. Ceder, J. Phase Equilib. 23, 348-359 (2002).</li>",
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"\t<p>In this example, you can perform a cluster expansion (CE) of the binary alloy Si<sub>x</sub>Ge<sub>1-x</sub>. By finding the optimal set of cluster, a model is built which allows numerically efficient predictions of the energy of any configuration (i.e. atomic arrangement) of the substitutional species Ge. Finally, you can perform a configurational sampling to predict the most stable configurations of the alloy.</p>",
"\t<!--p>",
"\t The figure below shows the 16-atoms supercell used to perform the study. An exemplary configuration of Ge atoms (blue spheres) is depicted",
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"",
"\t<p>The optimal set of clusters is selected out of a large pool of clusters through a cross validation procedure.</p>",
"",
"\t<p>By clicking <b>Settings</b> you can set the parameters determining the size and charachter of the pool of clusters. You can also set the parameters affecting the configurational sampling. A more detailed explanation of the different settings can be found below: </p>",
" <ul>",
" <li>Maximum radius (Angstrom): Maximum distance between any two crystal sites composing a cluster in the pool of clusters. </li>",
" <li>Maximum number of points: Maximum number of points (i.e. crystal sites) composing a cluster in the pool of clusters.</li>",
" <li>Number of sampling steps: Number of configurations sampled at every composition x.</li>",
" <li>Temperature (K): Temperature of the simulated annealing procedure used for sampling the configuration space. The sampling is based on a canonical Metropolis algorithm.</li>",
" <li>Number of concentrations to be sampled: Number of values of x (in the formula Si<sub>x</sub>Ge<sub>1-x</sub>) for which samplings are performed.</li>",
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"\t",
"\t",
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"\t<p>After the preferred settings have been adjusted, click <b>RUN</b> for performing the calculations. </p>",
"",
"\t<p>During the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: </p>",
"\t<ul>",
"\t <li> the cluster optimization procedure is plotted. Both the training-RMSE and cv-RMSE error are depicted. A red circle in the plot indicates the set of clusters leading to the lowest cv-RMSE. This is used to build the model to predict energies in the configurational sampling.</li>",
"\t <li> A plot of the energy of mixing versus composition is displayed. Both the <i>ab-initio</i>, fitted, sampled and lowest energy found are depicted.</li>",
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"<button type=\"button\" class=\"btn btn-default\" onclick='toggle_settings()'>",
" Settings",
"</button>",
"",
"<a target=\"_blank\" href=\"http://forum.analytics-toolkit.nomad-coe.eu/\" class=\"btn btn-primary\"> Tell us what you think</a>",
""
],
"hidden": true
},
"output": {
"state": {},
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\n \n \n var run_lasso = function() {\n $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");\n myFunc();\n //getFeatures();\n //getOperators();\n //beaker.max_dim = $(\"#lasso_max_dim_selector\").val();\n //beaker.structures_diff = $(\"#lasso_structures_diff\").val();\n //beaker.n_comb = $(\"#n_comb\").val();\n //beaker.n_sis = $(\"#n_sis\").val();\n //beaker.units = $(\"#units_select\").val();\n beaker.evaluate(\"calc_cell1\"); // evaluate cells with tag \"lasso_cell\"\n beaker.evaluate(\"calc_cell2\"); // evaluate cells with tag \"lasso_cell\"\n beaker.evaluate(\"calc_cell3\"); // evaluate cells with tag \"lasso_cell\"\n beaker.evaluate(\"calc_cell4\"); // evaluate cells with tag \"lasso_cell\"\n beaker.evaluate(\"calc_cell41\"); // evaluate cells with tag \"lasso_cell\"\n beaker.evaluate(\"calc_cell5\"); // evaluate cells with tag \"lasso_cell\"\n beaker.evaluate(\"calc_cell51\"); // evaluate cells with tag \"lasso_cell\"\n beaker.evaluate(\"calc_cell6\"); // evaluate cells with tag \"lasso_cell\"\n // view_result()\n };\n\n var 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 clearyy();\n };\n\n var 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\n var getOperators = function() {\n beaker.allowed_operations = [];\n $('#lasso_operators_select input:checkbox').each(function () {\n if(this.checked )\n beaker.allowed_operations.push(this.value);\n });\n }; \n\n var 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 };\n beaker.view_result = function(result_link) {\n // beaker.evaluate(\"lasso_viewer_result\").then(function(x) {\n $(\"#lasso_result_button\").attr(\"href\", result_link);\n // }); \n $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");\n }\n\n\n var myFunc = function() {\n beaker.cradius = document.getElementsByName(\"cradius\")[0].value;\n beaker.cpoints = document.getElementById(\"cpoints\").value;\n beaker.msteps = document.getElementsByName(\"msteps\")[0].value;\n beaker.mtemp = document.getElementsByName(\"mtemp\")[0].value;\n beaker.nsub = document.getElementsByName(\"nsub\")[0].value;\n };\n \n \n</script>\n\n\n\n\n<style type=\"text/css\">\n .lasso_instructions{\n font-size: 15px;\n }\n</style>\n\n<!-- Button trigger modal -->\n\n<!--button type=\"button\" onclick='clearyy()'>\n Clear all\n</button-->\n\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\t<p>Substitutional alloys, such as, <i>e.g.</i>, Si<sub>1-x</sub>Ge<sub>x</sub>, present a multitude of atomic arrangements, or configurations. Thus, it is impossible to perform numerically costly <i>ab-initio</i> calculations for every of them. The cluster-expansion (CE) technique allows for building numerically efficient models to predict the energy E of the alloy by exploiting the unique dependence of E on the configuration (<i>i.e.</i> the specific arrangement of Ge atoms in the crystal). It relies on a set of <i>ab-initio</i> calculations obtained by density-functional theory and has been applied to describe stable phases of bulk and surface alloys</p>\n\n\t<p>With this technique, the energy can be parametrized in terms of clusters (a set of crystal sites). The energy contribution of every of them is termed effective-cluster interaction (ECI). Thus, for an arbitrary configuration of the alloy, the clusters formed by the substitutional species are found and the sum of the respective ECIs gives a prediction of the energy for the given configuration. Each cluster is defined by a specific number and arrangement of crystal sites (empty cluster, 1-point clusters, ..., n-point clusters). </p>\n\t<p>\n\t Through a cross-validation procedure, the optimal set of clusters to represent the energy of the alloy are found. This set is then used to build a model which allows quick and accurate predictions of the energy for arbitrary configurations. Using this model, millions of structures can be sampled, and thus the ground state configurations can be found. The knowledge of the stable structures is of fundamental importance for the understanding of the physical properties of the alloys. \n\t</p>\n\t<p>\n\t The tool makes use of the python package <tt>CELL</tt> (Cluster Expansion for large parent ceLLs). An application of <tt>CELL</tt> for the prediction of stable phases of complex thermoelectric alloys, can be found in:</p>\n\t <div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\n\t M. Troppenz, S. Rigamonti, and C. Draxl: <span style=\"font-style: italic;\">Predicting Ground-State Configurations and Electronic Properties of the\n\t Thermoelectric Clathrates Ba<sub>8</sub>Al<sub>x</sub>Si<sub>46-x</sub> and Sr<sub>8</sub>Al<sub>x</sub>Si<sub>46-x</sub></span>, accepted for publication in Chemistry of Materials (2017).\n\t </div>\n\t\n\t<p><tt>CELL</tt> uses the <b>corrdump</b> utility of ATAT for the generation of clusters and the construction of the correlation matrix (A. van de Walle, CALPHAD: Comput. Coupling Phase Diagrams Thermochem. 33, 266-278 (2009))</p>\n\n\t<p>References:</p>\n <ol>\n <li>J. M. Sanchez, F. Ducastelle, and D. Gratias, Phys. A 128, 334-350 (1984).</li>\n <li>A. van de Walle and G. Ceder, J. Phase Equilib. 23, 348-359 (2002).</li>\n </ol>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n\t<!-- <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\" style=\"display: none;\">\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\t<p>In this example, you can perform a cluster expansion (CE) of the binary alloy Si<sub>x</sub>Ge<sub>1-x</sub>. By finding the optimal set of cluster, a model is built which allows numerically efficient predictions of the energy of any configuration (i.e. atomic arrangement) of the substitutional species Ge. Finally, you can perform a configurational sampling to predict the most stable configurations of the alloy.</p>\n\t<!--p>\n\t The figure below shows the 16-atoms supercell used to perform the study. An exemplary configuration of Ge atoms (blue spheres) is depicted\n\t <figure>\n\t <img src=\"/user/struc.png\" alt=\"Supercell\" style=\"width:150px;\" >\n\t </figure>\n \t</p-->\n\n\t<p>The optimal set of clusters is selected out of a large pool of clusters through a cross validation procedure.</p>\n\n\t<p>By clicking <b>Settings</b> you can set the parameters determining the size and charachter of the pool of clusters. You can also set the parameters affecting the configurational sampling. A more detailed explanation of the different settings can be found below: </p>\n <ul>\n <li>Maximum radius (Angstrom): Maximum distance between any two crystal sites composing a cluster in the pool of clusters. </li>\n <li>Maximum number of points: Maximum number of points (i.e. crystal sites) composing a cluster in the pool of clusters.</li>\n <li>Number of sampling steps: Number of configurations sampled at every composition x.</li>\n <li>Temperature (K): Temperature of the simulated annealing procedure used for sampling the configuration space. The sampling is based on a canonical Metropolis algorithm.</li>\n <li>Number of concentrations to be sampled: Number of values of x (in the formula Si<sub>x</sub>Ge<sub>1-x</sub>) for which samplings are performed.</li>\n </ul> \n\t\n\t\n\t\n\t<p>After the preferred settings have been adjusted, click <b>RUN</b> for performing the calculations. </p>\n\n\t<p>During the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: </p>\n\t<ul>\n\t <li> the cluster optimization procedure is plotted. Both the training-RMSE and cv-RMSE error are depicted. A red circle in the plot indicates the set of clusters leading to the lowest cv-RMSE. This is used to build the model to predict energies in the configurational sampling.</li>\n\t <li> A plot of the energy of mixing versus composition is displayed. Both the <i>ab-initio</i>, fitted, sampled and lowest energy found are depicted.</li>\n\t</ul>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n\t<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" onclick=\"toggle_settings()\">\n Settings\n</button>\n\n<a target=\"_blank\" href=\"http://forum.analytics-toolkit.nomad-coe.eu/\" class=\"btn btn-primary\"> Tell us what you think</a>\n"
}, "selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 72
},
"evaluatorReader": true,
"lineCount": 195
},
{
"id": "lasso-settings-cell",
"type": "code",
"evaluator": "HTML",
"input": {
"body": [
"<style type=\"text/css\">",
" #lasso-hidden-settings-div{",
" display:none;",
" } ",
" #lasso-hidden-settings-button{",
" display:none;",
" } ",
"</style>",
"",
"<div class=\"lasso_control\" id=\"lasso-hidden-settings-div\">",
" <div class=\"row\"> <!-- Start of third row-->",
" <p>Definition of the pool of clusters.</p>",
" <p class=\"lasso_selection_description\"><b>Maximum number of points: </b> ",
" <select id='cpoints'>",
"\t<option value=\"1\" > 1</option>",
"\t<option value=\"2\" > 2</option>",
"\t<option value=\"3\" selected=\"selected\" > 3</option>",
"\t<option value=\"4\"> 4</option>",
" </select>",
" </p>",
" <p class=\"lasso_selection_description\"> <b>Maximum radius (Angstrom):</b> ",
" <input name=\"cradius\" type=\"number\" step=\"0.1\" value=\"5.0\" min=\"0\"/>",
" </p>",
" </div><!-- End of row--> ",
" ",
" <div class=\"row\"> <!-- Start of third row-->",
" <p>Search for the ground states (configurational sampling).</p>",
" <p class=\"lasso_selection_description\"> <b>Number of sampling steps:</b> ",
" <input name=\"msteps\" type=\"number\" step=\"1\" value=\"200\" min=\"1\"/>",
" </p>",
" <p class=\"lasso_selection_description\"> <b>Temperature (K):</b>",
" <input name=\"mtemp\" type=\"number\" step=\"1\" value=\"1000\" min=\"1\"/>",
" </p>",
" <p class=\"lasso_selection_description\"> <b>Number of concentrations to be sampled:</b>",
" <input name=\"nsub\" type=\"number\" step=\"1\" value=\"13\" min=\"1\"/>",
" </p>",
" </div><!-- End of row--> ",
" ",
" ",
" <!-- <span title=''> <img src=\"http://images.clipartpanda.com/question-purzen_Icon_with_question_mark_Vector_Clipart.png\" style=\"height: 30px; width: 30px;\"> </span> -->",
" <!-- <button class=\"btn btn-default\" onclick='run_lasso()'>RUN LASSO+L0</button> -->",
" <!-- <button class=\"btn btn-default\" onclick='reset_lasso()'>RESET</button> -->",
" <!-- <label title=\"This button becomes active when the",
"\t run is finished. By clicking it, an interactive plot of the first 2",
"\t dimensions of the optimized descriptor will be opened\"> ",
"\t <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\" >View interactive 2D scatter plot</a> </label> -->",
"</div>",
""
],
"hidden": true
},
"output": {
"state": {},
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<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\n<div class=\"lasso_control\" id=\"lasso-hidden-settings-div\" style=\"display: none;\">\n <div class=\"row\"> <!-- Start of third row-->\n <p>Definition of the pool of clusters.</p>\n <p class=\"lasso_selection_description\"><b>Maximum number of points: </b> \n <select id=\"cpoints\">\n\t<option value=\"1\"> 1</option>\n\t<option value=\"2\"> 2</option>\n\t<option value=\"3\" selected=\"selected\"> 3</option>\n\t<option value=\"4\"> 4</option>\n </select>\n </p>\n <p class=\"lasso_selection_description\"> <b>Maximum radius (Angstrom):</b> \n <input name=\"cradius\" step=\"0.1\" value=\"5.0\" min=\"0\" type=\"number\">\n </p>\n </div><!-- End of row--> \n \n <div class=\"row\"> <!-- Start of third row-->\n <p>Search for the ground states (configurational sampling).</p>\n <p class=\"lasso_selection_description\"> <b>Number of sampling steps:</b> \n <input name=\"msteps\" step=\"1\" value=\"200\" min=\"1\" type=\"number\">\n </p>\n <p class=\"lasso_selection_description\"> <b>Temperature (K):</b>\n <input name=\"mtemp\" step=\"1\" value=\"1000\" min=\"1\" type=\"number\">\n </p>\n <p class=\"lasso_selection_description\"> <b>Number of concentrations to be sampled:</b>\n <input name=\"nsub\" step=\"1\" value=\"13\" min=\"1\" type=\"number\">\n </p>\n </div><!-- End of row--> \n \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 LASSO+L0</button> -->\n <!-- <button class=\"btn btn-default\" onclick='reset_lasso()'>RESET</button> -->\n <!-- <label title=\"This button becomes active when the\n\t run is finished. By clicking it, an interactive plot of the first 2\n\t dimensions of the optimized descriptor will be opened\"> \n\t <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\" >View interactive 2D scatter plot</a> </label> -->\n</div>\n" },
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 50
},
"evaluatorReader": true,
"lineCount": 48,
"tags": "lasso -settings"
},
{
"id": "codeaYKnSD",
"type": "code",
"evaluator": "HTML",
"input": {
"body": [
"<div class=\"lasso_control\">",
"",
" <p style=\"margin-top: 1ex;\"></p>",
" <button class=\"btn btn-default\" onclick='run_lasso()' style=\"font-weight: bold;\">",
" RUN",
" </button>",
" <div id=\"lasso-hidden-settings-button\">",
" <button class=\"btn btn-default\" onclick='reset_lasso()'>",
" RESET",
" </button>",
" </div>",
" <!--label title=\"This button becomes active when the",
"\t\trun is finished. By clicking it, an interactive plot of the first 2",
"\t\tdimensions of the optimized descriptor will be opened\"> ",
" <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\" >",
" View interactive 2D scatter plot",
" </a>",
" </label-->",
"</div>",
""
],
"hidden": true
},
"output": {
"state": {},
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<div class=\"lasso_control\">\n\n <p style=\"margin-top: 1ex;\"></p>\n <button class=\"btn btn-default\" onclick=\"run_lasso()\" style=\"font-weight: bold;\">\n RUN\n </button>\n <div id=\"lasso-hidden-settings-button\" style=\"display: none;\">\n <button class=\"btn btn-default\" onclick=\"reset_lasso()\">\n RESET\n </button>\n </div>\n <!--label title=\"This button becomes active when the\n\t\trun is finished. By clicking it, an interactive plot of the first 2\n\t\tdimensions of the optimized descriptor will be opened\"> \n <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\" >\n View interactive 2D scatter plot\n </a>\n </label-->\n</div>\n"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 93
},
"evaluatorReader": true,
"lineCount": 20
},
{
"id": "codejKFmIq",
"type": "code",
"evaluator": "HTML",
"input": {
"body": [
"<!--TAG: calc_cell1 -->",
"",
"<style type=\"text/css\">",
" #clearonload{",
" display:block;",
" } ",
"</style>",
"",
"<script>",
" beaker.toggle_outs=0;",
"</script>",
"", "<div class=\"clearonload\">",
" <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\">",
" Initialization ",
" </label>",
" <p>",
" A set of ab-initio calculations is collected, a pool of clusters is built, and the structures are analyzed to build a matrix of cluster-structure correlations.",
" </p>",
"</div> "
],
"hidden": true
},
"output": {
"state": {},
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<!--TAG: calc_cell1 -->\n\n<style type=\"text/css\">\n #clearonload{\n display:block;\n } \n</style>\n\n<script>\n beaker.toggle_outs=0;\n</script>\n\n<div class=\"clearonload\" style=\"display: none;\">\n <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\">\n Initialization \n </label>\n <p>\n A set of ab-initio calculations is collected, a pool of clusters is built, and the structures are analyzed to build a matrix of cluster-structure correlations.\n </p>\n</div> "
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 50
},
"evaluatorReader": true,
"lineCount": 20,
"tags": "calc_cell1"
},
{
"id": "codeUZxYCm",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [
"# TAG: calc_imports",
"basePath=\"/Software/cell\"",
"import sys",
"import os",
"#StringIO below will be used to redirect the print output of CELL to strings.",
"from cStringIO import StringIO",
"old_stdout = sys.stdout",
"sys.stdout = mystdout = StringIO()",
"",
"if not basePath in sys.path:",
" sys.path.insert(1,basePath)",
"os.environ[\"PATH\"]=\"/home/beaker/bin:\"+os.environ[\"PATH\"]",
"os.chdir(\"/home/beaker/test/sige1\")",
"",
"import cell.tasks as ct",
"import cell.plotting_utils as cpu",
"import cell as c"
],
"hidden": true
},
"output": {
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
"shellId": "C0108CE2A7F449938A45BA2B321EAD2F",
"elapsedTime": 515,
"height": 76
},
"evaluatorReader": true,
"lineCount": 17,
"tags": "calc_imports"
},
{
"id": "code001",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [ "# TAG: calc_cell2 ",
" ",
"#c.parse_core()",
"tsub = c.total_subs(lat_path=\"lat.in.met\")",
"print \"Number of substitutional sites in the supercell: \"+str(tsub)+\"\\n\"",
"",
"",
"if int(beaker.nsub) > tsub-1:",
" print \"Warning: Number of concentrations to be sampled (\"+beaker.nsub+\") exceeds the number of possible binary compositions.\\nThere are \"+str(int(tsub)-1)+\" binary compositions for a lattice with \"+str(tsub)+\" subsitutional sites.\\n\"",
"",
"print \"Step 1:\"",
"ct.write_ab_initio()",
"print \"Step 2:\"",
"ct.gen_clusters(radius=float(beaker.cradius),size=int(beaker.cpoints))",
"print \"Step 3:\"",
"ct.gen_correlations()"
],
"hidden": true
},
"output": {
"selectedType": "Hidden",
"outputArrived": true,
"elapsedTime": 4528,
"state": {},
"pluginName": "IPython",
"shellId": "F61CB8DBBED9443780E540C9A11AA76A",
"height": 272
},
"evaluatorReader": true,
"lineCount": 16,
"tags": "calc_cell2"
},
{
"id": "codeBNUb73",
"type": "code",
"evaluator": "HTML",
"input": {
"body": [
"<!--TAG: calc_cell3 -->",
"",
"<!--style type=\"text/css\">",
" #lasso-hidden-out{",
" display:block;",
" } ",
"</style-->",
"<div class=\"clearonload\">",
" <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\">",
" Cluster optimization ",
" </label>",
" <p>",
" Now, a cross-validation procedure is performed in order to find the optimal set of clusters. The optimal set leads to the best fit of the model to the ab-initio data, and at the same time avoids overfitting (i.e. fitting to noise in the ab-initio data). The selected set is marked with a red circle.",
" </p>",
"</div>"
],
"hidden": true
},
"output": {
"state": {},
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<!--TAG: calc_cell3 -->\n\n<!--style type=\"text/css\">\n #lasso-hidden-out{\n display:block;\n } \n</style-->\n<div class=\"clearonload\" style=\"display: none;\">\n <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\">\n Cluster optimization \n </label>\n <p>\n Now, a cross-validation procedure is performed in order to find the optimal set of clusters. The optimal set leads to the best fit of the model to the ab-initio data, and at the same time avoids overfitting (i.e. fitting to noise in the ab-initio data). The selected set is marked with a red circle.\n </p>\n</div>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 50
},
"evaluatorReader": true,
"lineCount": 15,
"tags": "calc_cell3" },
{
"id": "code6Q21r4",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [
"# TAG: calc_cell4",
"",
"if beaker.toggle_outs==1:",
" print \" \"",
"else: ",
" ct.cluster_optimization()",
" #print \"Optimal set of clusters found through cross-validation procedure. The optimal set is marked with a red circle.\"",
" cpu.plot_optimization_mpl()"
],
"hidden": true
},
"output": {
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
"shellId": "C0108CE2A7F449938A45BA2B321EAD2F",
"elapsedTime": 493,
"height": 619
},
"evaluatorReader": true,
"lineCount": 8,
"tags": "calc_cell4"
},
{
"id": "codeTe1bjE",
"type": "code",
"evaluator": "HTML",
"input": {
"body": [
"<div class=\"clearonload\">",
" <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\">",
" Configurational sampling ",
" </label>",
" <p>",
" Now, a configurational sampling is performed. The duration of this task is proportional to the number of compositions to be sampled and the number of sampling steps entered in the \"Settings\" form.",
" </p>",
"</div>"
],
"hidden": true
},
"output": {
"state": {},
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<div class=\"clearonload\" style=\"display: none;\">\n <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\">\n Configurational sampling \n </label>\n <p>\n Now, a configurational sampling is performed. The duration of this task is proportional to the number of compositions to be sampled and the number of sampling steps entered in the \"Settings\" form.\n </p>\n</div>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 50
},
"evaluatorReader": true,
"lineCount": 8,
"tags": "calc_cell41"
},
{
"id": "codeYZHUBi",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [
"# TAG: calc_cell5",
" ", "nsub_list=[]",
"if int(beaker.nsub)>tsub-1:",
" nsub_list = range(1,tsub)",
"else:",
" for i in range(1,int(beaker.nsub)+1):",
" sub=int(float(i/(int(beaker.nsub)*1.0+1)*tsub))",
" nsub_list.append(sub)",
"",
"",
"ct.gs_search(nsub_list = nsub_list, temp=int(beaker.mtemp), nsteps=int(beaker.msteps))",
"#print \"Sampling finished.\""
],
"hidden": true
},
"output": {
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
"shellId": "F61CB8DBBED9443780E540C9A11AA76A",
"elapsedTime": 33644,
"height": 3116
},
"evaluatorReader": true,
"lineCount": 13,
"tags": "calc_cell5"
},
{
"id": "codeCAuLOn",
"type": "code",
"evaluator": "HTML",
"input": {
"body": [
"<div class=\"clearonload\">",
" <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\">",
" Predicted ground states ",
" </label>",
" <p>",
" Configurational sampling finished. The ground states found by the sampling procedure are represented in the plot below by red circles joined by a red solid line. The figure shows the energy of mixing, <i>i.e.</i> the energy difference between the sampled configurations of the compound Si<sub>x</sub>Ge<sub>1-x</sub> and a phase-separated mix of the pure compounds Si and Ge with composition x. The quality of the cluster-expansion model can be appreciated by the good agreement between <i>ab-initio</i> (black circles) and fitted (black dots) values. The sampled configurations are indicated by red points.",
" </p>",
"</div>"
],
"hidden": true
},
"output": {
"state": {},
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<div class=\"clearonload\" style=\"display: none;\">\n <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\">\n Predicted ground states \n </label>\n <p>\n Configurational sampling finished. The ground states found by the sampling procedure are represented in the plot below by red circles joined by a red solid line. The figure shows the energy of mixing, <i>i.e.</i> the energy difference between the sampled configurations of the compound Si<sub>x</sub>Ge<sub>1-x</sub> and a phase-separated mix of the pure compounds Si and Ge with composition x. The quality of the cluster-expansion model can be appreciated by the good agreement between <i>ab-initio</i> (black circles) and fitted (black dots) values. The sampled configurations are indicated by red points.\n </p>\n</div>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 50
},
"evaluatorReader": true,
"lineCount": 8,
"tags": "calc_cell51"
},
{
"id": "codeGXuYGc",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [
"# TAG: calc_cell6",
"",
"if beaker.toggle_outs==1:",
" print \" \"",
"else: ",
" cpu.plot_gs_search_mpl()" ],
"hidden": true
},
"output": {
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
"shellId": "C0108CE2A7F449938A45BA2B321EAD2F",
"elapsedTime": 437,
"height": 619
},
"evaluatorReader": true,
"lineCount": 6,
"tags": "calc_cell6"
}
],
"namespace": {
"tsub": 16,
"cradius": "5.0",
"cpoints": "3",
"msteps": "10",
"mtemp": "1000",
"nsub": "13",
"toggle_outs": 1
},
"locked": true
}