Best practices in machine learning for chemistry.

Saved in:
Bibliographic Details
Title: Best practices in machine learning for chemistry.
Authors: Artrith N; Department of Chemical Engineering, Columbia University, New York, NY, USA. na2782@columbia.edu.; Columbia Center for Computational Electrochemistry (CCCE), Columbia University, New York, NY, USA. na2782@columbia.edu., Butler KT; SciML, Scientific Computing Department, STFC Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK. keith.butler@stfc.ac.uk., Coudert FX; Chimie ParisTech, PSL University, CNRS, Institut de Recherche de Chimie Paris, Paris, France. fx.coudert@chimieparistech.psl.eu., Han S; Department of Materials Science and Engineering, Seoul National University, Seoul, Korea. hansw@snu.ac.kr., Isayev O; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, PA, USA. olexandr@olexandrisayev.com.; Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA. olexandr@olexandrisayev.com., Jain A; Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California, USA. ajain@lbl.gov., Walsh A; Department of Materials, Imperial College London, London, UK. a.walsh@imperial.ac.uk.; Department of Materials Science and Engineering, Yonsei University, Seoul, Korea. a.walsh@imperial.ac.uk.
Source: Nature chemistry [Nat Chem] 2021 Jun; Vol. 13 (6), pp. 505-508.
Publication Type: Journal Article
Journal Info: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101499734 Publication Model: Print Cited Medium: Internet ISSN: 1755-4349 (Electronic) Linking ISSN: 17554330 NLM ISO Abbreviation: Nat Chem Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1755-4349
DOI:10.1038/s41557-021-00716-z