diff --git a/metainfo.json b/metainfo.json index 79de625af7d047a62f73a97bcc856817a93877e7..899385cce224c721727b7dd4b15ff3515ba61de5 100644 --- a/metainfo.json +++ b/metainfo.json @@ -1,34 +1,38 @@ { "authors": [ - "Sbailo, Luigi", - "Ghiringhelli, Luca M." + "Sbailò, Luigi", + "Purcell, Thomas A. R.", + "Ghiringhelli, Luca M.", + "Scheffler, Matthias" ], "email": "ghiringhelli@fhi-berlin.mpg.de", - "title": "Artificial intelligence for high-throughput discovery of topological insulators", - "description": "In this tutorial... ", + "title": "Discovery of new topological insulators in alloyed tetradymites", + "description": "Learn how to find descriptive parameters (short formulas) that predict whether alloyed materials are topological or trivial insulators, using the example of tetradymites. This notebook is based on the algorithm 'sure independence screening and sparsifying operator' (SISSO) that enables to search for optimal descriptor by scanning huge feature spaces.", "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-tetradymite-PRM2020", "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/tetradymite_PRM2020.ipynb", "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/tetradymite_PRM2020.ipynb", - "updated": "2020-26-08", + "updated": "2020-12-09", "flags":{ "featured": true, "top_of_list": false }, "labels": { "application_keyword": [ - "Something" + "Tetradymites", + "Topological insulators" ], "application_section": [ - "Something" + "Timely artificial-intelligence applications to Materials Science" ], "application_system": [ - "Something" + "Tetradymites" ], "category": [ - "Something" + "Tutorial" ], "data_analytics_method": [ - "Something" + "SISSO", + "Classification" ], "platform": [ "jupyter" diff --git a/tetradymite_PRM2020.ipynb b/tetradymite_PRM2020.ipynb index 395e431f2edc3fb957cf32f3c716ecdba9f93639..0f1f4b0823d5da9bb7d959060999235ff43c1936 100644 --- a/tetradymite_PRM2020.ipynb +++ b/tetradymite_PRM2020.ipynb @@ -19,7 +19,7 @@ "\n", " \n", " <div style=\"text-align:center\">\n", - " <b><font size=\"6.4\">Discovery of new topological insulators in alloyed tetradymites with symbolic regression combined with compressed sensing (SISSO).\n", + " <b><font size=\"6.4\">Discovery of new topological insulators in alloyed tetradymites\n", " </font></b> \n", " </div>\n", "\n", @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T16:51:20.970400Z", @@ -100,37 +100,7 @@ }, "init_cell": true }, - "outputs": [ - { - "data": { - "text/html": [ - "<script>\n", - " code_show=true; \n", - " function code_toggle() {\n", - " if (code_show)\n", - " {\n", - " $('div.input').hide();\n", - " } \n", - " else \n", - " {\n", - " $('div.input').show();\n", - " }\n", - " code_show = !code_show\n", - " } \n", - " $( document ).ready(code_toggle);\n", - " window.runCells(\"startup\");\n", - "</script>\n", - "The Python code for this notebook is by default hidden for easier reading.\n", - "To toggle on/off the code, click <a href=\"javascript:code_toggle()\">here</a>.\n" - ], - "text/plain": [ - "<IPython.core.display.HTML object>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "%%HTML\n", "<script>\n", @@ -155,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T16:27:16.278570Z", @@ -170,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T16:51:20.988129Z", @@ -209,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T16:51:21.019833Z", @@ -279,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T16:51:21.154094Z", @@ -323,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T16:51:21.173521Z", @@ -391,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T16:51:21.204474Z", @@ -531,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T16:51:21.475170Z", @@ -540,22 +510,7 @@ "init_cell": true, "scrolled": false }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "88675c3ce6594422ae31faaabc0b1b30", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HBox(children=(VBox(children=(Label(value=''), Checkbox(value=True, disabled=True, indent=False…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cb_layout = widgets.Layout(width = '15px')\n", "thin_layout = widgets.Layout(width = '100px')\n", @@ -659,7 +614,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.3" } }, "nbformat": 4,