Best practices in machine learning for chemistry comment

被引：327

作者：

Artrith, Nongnuch ^{[1
,2
]}

Butler, Keith T. ^{[3
]}

Coudert, Francois-Xavier ^{[4
]}

Han, Seungwu ^{[5
]}

Isayev, Olexandr ^{[6
,7
]}

Jain, Anubhav ^{[8
]}

Walsh, Aron ^{[9
,10
]}

机构：

[1] Columbia Univ, Dept Chem Engn, New York, NY 10027 USA

[2] Columbia Univ, Columbia Ctr Computat Electrochem, CCCE, New York, NY 10027 USA

[3] STFC Rutherford Appleton Lab, Dept Comp Sci, SciML, Harwell Campus, Didcot, England

[4] PSL Univ, Inst Rech Chim Paris, Chim ParisTech, CNRS, Paris, France

[5] Seoul Natl Univ, Dept Mat Sci & Engn, Seoul, South Korea

[6] Carnegie Mellon Univ, Sch Comp Sci, Computat Biol Dept, Pennsylvania, PA 15213 USA

[7] Carnegie Mellon Univ, Mellon Coll Sci, Dept Chem, Pittsburgh, PA 15213 USA

[8] Lawrence Berkeley Natl Lab, Energy Technol Area, Berkeley, CA 94720 USA

[9] Imperial Coll London, Dept Mat, London, England

[10] Yonsei Univ, Dept Mat Sci & Engn, Seoul, South Korea

来源：

NATURE CHEMISTRY | 2021年 / 13卷 / 06期

关键词：

PERFORMANCE; VALIDATION; NETWORKS;

D O I：

10.1038/s41557-021-00716-z

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Statistical tools based on machine learning are becoming integrated into chemistry research workflows. We discuss the elements necessary to train reliable, repeatable and reproducible models, and recommend a set of guidelines for machine learning reports.

引用

页码：505 / 508

页数：4

共 34 条

[1] Predicting reaction performance in C-N cross-coupling using machine learning [J].

Ahneman, Derek T. ;

Estrada, Jesus G. ;

Lin, Shishi ;

Dreher, Spencer D. ;

Doyle, Abigail G. .

SCIENCE, 2018, 360 (6385) :186-190

[2]

[Anonymous], REPRODUCIBILITY REPL

[3] Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm [J].

Artrith, Nongnuch ;

Urban, Alexander ;

Ceder, Gerbrand .

JOURNAL OF CHEMICAL PHYSICS, 2018, 148 (24)

[4] Charting a course for chemistry [J].

Aspuru-Guzik, Alan ;

Baik, Mu-Hyun ;

Balasubramanian, Shankar ;

Banerjee, Rahul ;

Bart, Suzanne ;

Borduas-Dedekind, Nadine ;

Chang, Sukbok ;

Chen, Peng ;

Corminboeuf, Clemence ;

Coudert, Francois-Xavier ;

Cronin, Leroy ;

Crudden, Cathleen ;

Cuk, Tanja ;

Doyle, Abigail G. ;

Fan, Chunhai ;

Feng, Xinliang ;

Freedman, Danna ;

Furukawa, Shuhei ;

Ghosh, Suhrit ;

Glorius, Frank ;

Jeffries-EL, Malika ;

Katsonis, Nathalie ;

Li, Ang ;

Linse, Sara Snogerup ;

Marchesan, Silvia ;

Maulide, Nuno ;

Milo, Anat ;

Narayan, Alison R. H. ;

Naumov, Pance ;

Nevado, Cristina ;

Nyokong, Tebello ;

Palacin, Rosa ;

Reid, Marc ;

Robinson, Carol ;

Robinson, Gregory ;

Sarpong, Richmond ;

Schindler, Corinna ;

Schlau-Cohen, Gabriela S. ;

Schmidt, Timothy W. ;

Sessoli, Roberta ;

Shao-Horn, Yang ;

Sleiman, Hanadi ;

Sutherland, John ;

Taylor, Annette ;

Tezcan, Akif ;

Tortosa, Mariola ;

Walsh, Aron ;

Watson, Allan J. B. ;

Weckhuysen, Bert M. ;

Weiss, Emily .

NATURE CHEMISTRY, 2019, 11 (04) :286-294

[5] First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems [J].

Behler, Joerg .

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 2017, 56 (42) :12828-12840

[6] Permutationally invariant potential energy surfaces in high dimensionality [J].

Braams, Bastiaan J. ;

Bowman, Joel M. .

INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY, 2009, 28 (04) :577-606

[7] Machine learning for molecular and materials science [J].

Butler, Keith T. ;

Davies, Daniel W. ;

Cartwright, Hugh ;

Isayev, Olexandr ;

Walsh, Aron .

NATURE, 2018, 559 (7715) :547-555

[8] Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals [J].

Chen, Chi ;

Ye, Weike ;

Zuo, Yunxing ;

Zheng, Chen ;

Ong, Shyue Ping .

CHEMISTRY OF MATERIALS, 2019, 31 (09) :3564-3572

[9] Systematic exploration of the mechanical properties of 13 621 inorganic compounds [J].

Chibani, Siwar ;

Coudert, Francois-Xavier .

CHEMICAL SCIENCE, 2019, 10 (37) :8589-8599

[10] Comment on "Predicting reaction performance in C-N cross-coupling using machine learning" [J].

Chuang, Kangway V. ;

Keiser, Michael J. .

SCIENCE, 2018, 362 (6416)

← 1 2 3 4 →