Machine learning for molecular and materials science

被引：3109

作者：

Butler, Keith T. ^{[1
]}

Davies, Daniel W. ^{[2
]}

Cartwright, Hugh ^{[3
]}

Isayev, Olexandr ^{[4
]}

Walsh, Aron ^{[5
,6
]}

机构：

[1] Rutherford Appleton Lab, ISIS Facil, Harwell Campus, Harwell, Berks, England

[2] Univ Bath, Dept Chem, Bath, Avon, England

[3] Univ Oxford, Dept Chem, Oxford, England

[4] Univ North Carolina Chapel Hill, Eshelman Sch Pharm, Chapel Hill, NC USA

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

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

来源：

NATURE | 2018年 / 559卷 / 7715期

基金：

英国工程与自然科学研究理事会;

关键词：

COMPUTATIONAL CHEMISTRY; NEURAL-NETWORKS; DISCOVERY; DESIGN; COMPUTER;

D O I：

10.1038/s41586-018-0337-2

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.

引用

页码：547 / 555

页数：9

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