Machine learning for molecular and materials science

被引：2975

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

共 100 条

[1] Perspective: Materials informatics and big data: Realization of the "fourth paradigm" of science in materials science [J].

Agrawal, Ankit ;

Choudhary, Alok .

APL MATERIALS, 2016, 4 (05)

[2] Low Data Drug Discovery with One-Shot Learning [J].

Altae-Tran, Han ;

Ramsundar, Bharath ;

Pappu, Aneesh S. ;

Pande, Vijay .

ACS CENTRAL SCIENCE, 2017, 3 (04) :283-293

[3]

[Anonymous], 2004, Kernel Methods for Pattern Analysis

[4] Stable and Efficient Linear Scaling First-Principles Molecular Dynamics for 10000+Atoms [J].

Arita, Michiaki ;

Bowler, David R. ;

Miyazaki, Tsuyoshi .

JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2014, 10 (12) :5419-5425

[5] Simulated quantum computation of molecular energies [J].

Aspuru-Guzik, A ;

Dutoi, AD ;

Love, PJ ;

Head-Gordon, M .

SCIENCE, 2005, 309 (5741) :1704-1707

[6] Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons [J].

Bartok, Albert P. ;

Payne, Mike C. ;

Kondor, Risi ;

Csanyi, Gabor .

PHYSICAL REVIEW LETTERS, 2010, 104 (13)

[7] 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

[8] Quantum machine learning [J].

Biamonte, Jacob ;

Wittek, Peter ;

Pancotti, Nicola ;

Rebentrost, Patrick ;

Wiebe, Nathan ;

Lloyd, Seth .

NATURE, 2017, 549 (7671) :195-202

[9] The problem with determining atomic structure at the nanoscale [J].

Billinge, Simon J. L. ;

Levin, Igor .

SCIENCE, 2007, 316 (5824) :561-565

[10]

Bonchev D, 1991, Chemical Graph Theory: Introduction and Fundamentals

← 1 2 3 4 5 6 7 8 9 10 →