Commit 833733cc authored by Qaem Hassanzada's avatar Qaem Hassanzada
Browse files

Fig 4 of the article is added

parents b90023f5 addcb202
{
"authors": [
"Lucas Foppa",
"Thomas Purcell",
"Hassanzada, Qaem",
"Foppa, Lucas",
"Bartel, Christopher",
"Purcell, Thomas",
"Sbailò, Luigi",
"Christopher Bartel",
"Ghiringhelli, Luca M."
],
"email": "ghiringhelli@fhi-berlin.mpg.de",
"title": "Finding a tolerance factor to predict perovskite stability with SISSO",
"description": "In this tutorial...",
"description": "This tutorial shows how a tolerance factor for predicting perovskite stability can be learned from data with the sure-independece-screening-and-sparsifying-operator (SISSO) descriptor-identification approach.",
"notebook_name": "perovskites_tolerance_factor.ipynb",
"url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-perovskite-tolerance-factor",
"link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/{tutorial}.ipynb",
"link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/{tutorial}.ipynb",
"updated": "2020-04-09",
"link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/perovskites_tolerance_factor.ipynb",
"link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/perovskites_tolerance_factor.ipynb",
"link_paper": "https://advances.sciencemag.org/content/advances/5/2/eaav0693.full.pdf",
"link_doi_paper": "https://doi.org/10.1126/sciadv.aav0693",
"updated": "2021-12-14",
"flags":{
"featured": true,
"top_of_list": false
"top_of_list": false,
"paper": true
},
"labels": {
"application_keyword": [
"SISSO"
],
"application_section": [
"Materials property prediction"
"Timely artificial-intelligence applications to materials science"
],
"category": [
"advanced_tutorial"
],
"application_system": [
"Perovskite"
],
"category": [
"Tutorial"
],
"data_analytics_method": [
"SISSO"
"ai_methods": [
"Supervised learning",
"Classification",
"Symbolic regression",
"Compressed sensing",
"SISSO",
"Decision tree",
"Features selection",
"Atomic features"
],
"platform": [
"jupyter"
......
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