From 81cd97f6cf807cbe57c1c4bbab8ec55570f5902f Mon Sep 17 00:00:00 2001 From: Luigi <luigi.sbailo@physik.hu-berlin.de> Date: Mon, 14 Mar 2022 21:40:50 +0000 Subject: [PATCH] CI: Update metainfo in the GUI --- src/toolkitMetadata.json | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/src/toolkitMetadata.json b/src/toolkitMetadata.json index 1afbcfb..93a8fbb 100644 --- a/src/toolkitMetadata.json +++ b/src/toolkitMetadata.json @@ -869,6 +869,44 @@ ] } }, + { + "authors": [ + "Foppa, Lucas", + "Ghiringhelli, Luca M.", + "Scheffler, Matthias" + ], + "email": "foppa@fhi-berlin.mpg.de", + "title": "Learning Design Rules for Catalysts from High-Throughput Experimentation and Theory via Subgroup Discovery", + "description": "This tutorial explores the application of subgroup discovery (SGD) to an experimental-theoretical data set in order to identify rules on key physicochemical parameters that describe the materials and environmental conditions associated with outstanding performance in heterogeneous catalysis.", + "notebook_name": "sgd_propylene_oxidation_hte.ipynb", + "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-sgd-propylene-oxidation-hte", + "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/sgd_propylene_oxidation_hte.ipynb", + "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/sgd_propylene_oxidation_hte.ipynb", + "link_paper": "https://pubs.acs.org/doi/10.1021/acscatal.1c04793", + "link_doi_paper": "https://pubs.acs.org/doi/10.1021/acscatal.1c04793", + "updated": "2022-2-09", + "flags": { + "featured": true, + "top_of_list": false + }, + "labels": { + "application_section": [ + "Timely artificial-intelligence applications to Materials Science" + ], + "application_system": [ + "Heterogeneous catalysis" + ], + "category": [ + "advanced_tutorial" + ], + "ai_methods": [ + "Subgroup discovery" + ], + "platform": [ + "jupyter" + ] + } + }, { "authors": [ "Cs\u00e1nyi, G\u00e1bor", -- GitLab