"This tutorial shows how to find descriptive parameters (short formulas) for the classification of materials properties. As an example, we address the classification of elemental and binary systems A$_x$B$_y$ into metals and non metals using experimental data extracted from the SpringerMaterials data base. The method is based on the algorithm <u>s</u>ure <i><u>i</u>ndependence <u>s</u>creening and <u>s</u>parsifying <u>o</u>perator</i> (SISSO), which enables to search for optimal descriptors by scanning huge feature spaces. ",
"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>",
"R. Ouyang, S. Curtarolo, E. Ahmetcik, M. Scheffler, L. M. Ghiringhelli: <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>",
"You can download the code <a href=\"https://github.com/rouyang2017/SISSO\">here</a> .",
"</div>",
" Click first <b>Reference settings</b> and afterwards <b>RUN</b> to reproduce the results from this publication; click <b>Background</b> for an explanation of the approach; or, modify <b>Settings</b> to produce your own results.",
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@@ -168,7 +168,7 @@
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" <p>We present a tool for predicting the metal-insulator classification of elemental 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:",
"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>",
"R. Ouyang, S. Curtarolo, E. Ahmetcik, M. Scheffler, L. M. Ghiringhelli: <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>",
"By running the tutorial with the reference settings ( click <b>Reference settings</b> , then <b>RUN</b>) the results of this publication can be recovered. In particular, by clicking on “View interactive 2D plot”, an interactive classification map (a chart where metals and non metals are separated into different regions of a low-dimensional representation, here two dimensional) will be opened in a new tab, similar to the following ( Fig. 3. (a) in <a href=\" https://arxiv.org/abs/1710.03319\">https://arxiv.org/abs/1710.03319</a>):",
