Best practices in machine learning for chemistry
Nat Chem
.
2021 Jun;13(6):505-508.
doi: 10.1038/s41557-021-00716-z.
Authors
Nongnuch Artrith
1
2
,
Keith T Butler
3
,
François-Xavier Coudert
4
,
Seungwu Han
5
,
Olexandr Isayev
6
7
,
Anubhav Jain
8
,
Aron Walsh
9
10
Affiliations
1
Department of Chemical Engineering, Columbia University, New York, NY, USA. na2782@columbia.edu.
2
Columbia Center for Computational Electrochemistry (CCCE), Columbia University, New York, NY, USA. na2782@columbia.edu.
3
SciML, Scientific Computing Department, STFC Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK. keith.butler@stfc.ac.uk.
4
Chimie ParisTech, PSL University, CNRS, Institut de Recherche de Chimie Paris, Paris, France. fx.coudert@chimieparistech.psl.eu.
5
Department of Materials Science and Engineering, Seoul National University, Seoul, Korea. hansw@snu.ac.kr.
6
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, PA, USA. olexandr@olexandrisayev.com.
7
Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA. olexandr@olexandrisayev.com.
8
Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California, USA. ajain@lbl.gov.
9
Department of Materials, Imperial College London, London, UK. a.walsh@imperial.ac.uk.
10
Department of Materials Science and Engineering, Yonsei University, Seoul, Korea. a.walsh@imperial.ac.uk.
PMID:
34059804
DOI:
10.1038/s41557-021-00716-z
No abstract available