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| 001 | 1020061 | ||
| 005 | 20250401102818.0 | ||
| 024 | 7 | _ | |a 10.5281/ZENODO.10430261 |2 doi |
| 037 | _ | _ | |a FZJ-2023-05862 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Wasmer, Johannes |0 P:(DE-Juel1)186072 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Best of Atomistic Machine Learning |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Website |2 DINI |
| 336 | 7 | _ | |a Web Page |0 12 |2 EndNote |
| 336 | 7 | _ | |a ONLINE |2 BibTeX |
| 336 | 7 | _ | |a ONLINE_RESOURCE |2 ORCID |
| 336 | 7 | _ | |a Output Types/Website |2 DataCite |
| 336 | 7 | _ | |a Website |b web |m web |0 PUB:(DE-HGF)37 |s 1704267620_15364 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a A ranked list of awesome atomistic machine learning projects. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a AIDAS - Joint Virtual Laboratory for AI, Data Analytics and Scalable Simulation (aidas_20200731) |0 G:(DE-Juel-1)aidas_20200731 |c aidas_20200731 |x 1 |
| 536 | _ | _ | |a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) |0 G:(DE-Juel1)HDS-LEE-20190612 |c HDS-LEE-20190612 |x 2 |
| 536 | _ | _ | |a DFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487) |0 G:(GEPRIS)491111487 |c 491111487 |x 3 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a ai4science |2 Other |
| 650 | _ | 7 | |a atomistic-machine-learning |2 Other |
| 650 | _ | 7 | |a scientific-machine-learning |2 Other |
| 650 | _ | 7 | |a community-resource |2 Other |
| 650 | _ | 7 | |a living-document |2 Other |
| 650 | _ | 7 | |a best-of-list |2 Other |
| 650 | _ | 7 | |a awesome-list |2 Other |
| 650 | _ | 7 | |a molecular-dynamics |2 Other |
| 650 | _ | 7 | |a density-functional-theory |2 Other |
| 650 | _ | 7 | |a computational-materials-science |2 Other |
| 650 | _ | 7 | |a computational-chemistry |2 Other |
| 650 | _ | 7 | |a quantum-chemistry |2 Other |
| 650 | _ | 7 | |a materials-discovery |2 Other |
| 650 | _ | 7 | |a materials-informatics |2 Other |
| 650 | _ | 7 | |a drug-discovery |2 Other |
| 650 | _ | 7 | |a surrogate-models |2 Other |
| 650 | _ | 7 | |a electronic-structure |2 Other |
| 650 | _ | 7 | |a interatomic-potentials |2 Other |
| 650 | _ | 7 | |a materials-datasets |2 Other |
| 650 | _ | 7 | |a chemistry-datasets |2 Other |
| 700 | 1 | _ | |a Evans, Matthew |0 0000-0002-1182-9098 |b 1 |e Contributor |
| 700 | 1 | _ | |a Blaiszik, Ben |0 0000-0002-5326-4902 |b 2 |e Contributor |
| 700 | 1 | _ | |a Riebesell, Janosh |0 P:(DE-HGF)0 |b 3 |
| 773 | _ | _ | |a 10.5281/ZENODO.10430261 |
| 856 | 4 | _ | |u https://github.com/JuDFTteam/best-of-atomistic-machine-learning |
| 909 | C | O | |o oai:juser.fz-juelich.de:1020061 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)186072 |
| 910 | 1 | _ | |a Université catholique de Louvain |0 I:(DE-HGF)0 |b 1 |6 0000-0002-1182-9098 |
| 910 | 1 | _ | |a University of Chicago |0 I:(DE-HGF)0 |b 2 |6 0000-0002-5326-4902 |
| 910 | 1 | _ | |a Cambridge University |0 I:(DE-HGF)0 |b 3 |6 P:(DE-HGF)0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-1-20090406 |k IAS-1 |l Quanten-Theorie der Materialien |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)PGI-1-20110106 |k PGI-1 |l Quanten-Theorie der Materialien |x 1 |
| 980 | _ | _ | |a web |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-1-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)PGI-1-20110106 |
| 980 | _ | _ | |a UNRESTRICTED |
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