001020061 001__ 1020061 001020061 005__ 20250401102818.0 001020061 0247_ $$2doi$$a10.5281/ZENODO.10430261 001020061 037__ $$aFZJ-2023-05862 001020061 041__ $$aEnglish 001020061 1001_ $$0P:(DE-Juel1)186072$$aWasmer, Johannes$$b0$$eCorresponding author 001020061 245__ $$aBest of Atomistic Machine Learning 001020061 260__ $$c2023 001020061 3367_ $$2DINI$$aWebsite 001020061 3367_ $$012$$2EndNote$$aWeb Page 001020061 3367_ $$2BibTeX$$aONLINE 001020061 3367_ $$2ORCID$$aONLINE_RESOURCE 001020061 3367_ $$2DataCite$$aOutput Types/Website 001020061 3367_ $$0PUB:(DE-HGF)37$$2PUB:(DE-HGF)$$aWebsite$$bweb$$mweb$$s1704267620_15364 001020061 520__ $$aA ranked list of awesome atomistic machine learning projects. 001020061 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0 001020061 536__ $$0G:(DE-Juel-1)aidas_20200731$$aAIDAS - Joint Virtual Laboratory for AI, Data Analytics and Scalable Simulation (aidas_20200731)$$caidas_20200731$$x1 001020061 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x2 001020061 536__ $$0G:(GEPRIS)491111487$$aDFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x3 001020061 588__ $$aDataset connected to DataCite 001020061 650_7 $$2Other$$aai4science 001020061 650_7 $$2Other$$aatomistic-machine-learning 001020061 650_7 $$2Other$$ascientific-machine-learning 001020061 650_7 $$2Other$$acommunity-resource 001020061 650_7 $$2Other$$aliving-document 001020061 650_7 $$2Other$$abest-of-list 001020061 650_7 $$2Other$$aawesome-list 001020061 650_7 $$2Other$$amolecular-dynamics 001020061 650_7 $$2Other$$adensity-functional-theory 001020061 650_7 $$2Other$$acomputational-materials-science 001020061 650_7 $$2Other$$acomputational-chemistry 001020061 650_7 $$2Other$$aquantum-chemistry 001020061 650_7 $$2Other$$amaterials-discovery 001020061 650_7 $$2Other$$amaterials-informatics 001020061 650_7 $$2Other$$adrug-discovery 001020061 650_7 $$2Other$$asurrogate-models 001020061 650_7 $$2Other$$aelectronic-structure 001020061 650_7 $$2Other$$ainteratomic-potentials 001020061 650_7 $$2Other$$amaterials-datasets 001020061 650_7 $$2Other$$achemistry-datasets 001020061 7001_ $$00000-0002-1182-9098$$aEvans, Matthew$$b1$$eContributor 001020061 7001_ $$00000-0002-5326-4902$$aBlaiszik, Ben$$b2$$eContributor 001020061 7001_ $$0P:(DE-HGF)0$$aRiebesell, Janosh$$b3 001020061 773__ $$a10.5281/ZENODO.10430261 001020061 8564_ $$uhttps://github.com/JuDFTteam/best-of-atomistic-machine-learning 001020061 909CO $$ooai:juser.fz-juelich.de:1020061$$pVDB 001020061 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186072$$aForschungszentrum Jülich$$b0$$kFZJ 001020061 9101_ $$0I:(DE-HGF)0$$60000-0002-1182-9098$$a Université catholique de Louvain$$b1 001020061 9101_ $$0I:(DE-HGF)0$$60000-0002-5326-4902$$a University of Chicago$$b2 001020061 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Cambridge University$$b3 001020061 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0 001020061 9141_ $$y2023 001020061 920__ $$lyes 001020061 9201_ $$0I:(DE-Juel1)IAS-1-20090406$$kIAS-1$$lQuanten-Theorie der Materialien$$x0 001020061 9201_ $$0I:(DE-Juel1)PGI-1-20110106$$kPGI-1$$lQuanten-Theorie der Materialien$$x1 001020061 980__ $$aweb 001020061 980__ $$aVDB 001020061 980__ $$aI:(DE-Juel1)IAS-1-20090406 001020061 980__ $$aI:(DE-Juel1)PGI-1-20110106 001020061 980__ $$aUNRESTRICTED