Machine learning for molecular and materials science

Keith T. Butler; Daniel W. Davies; Hugh Cartwright; Olexandr Isayev; Aron Walsh

doi:10.1038/s41586-018-0337-2

Machine learning for molecular and materials science

Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh

Department of Physics

Research output: Contribution to journal › Review article › peer-review

2935 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	547-555
Number of pages	9
Journal	Nature
Volume	559
Issue number	7715
DOIs	https://doi.org/10.1038/s41586-018-0337-2
State	Published - 26 Jul 2018

Bibliographical note

Publisher Copyright:
© 2018, Macmillan Publishers Ltd., part of Springer Nature.

Access to Document

10.1038/s41586-018-0337-2

Cite this

@article{6a04eb5c1021423787d22bc29d582c63,

title = "Machine learning for molecular and materials science",

abstract = "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.",

author = "Butler, {Keith T.} and Davies, {Daniel W.} and Hugh Cartwright and Olexandr Isayev and Aron Walsh",

note = "Publisher Copyright: {\textcopyright} 2018, Macmillan Publishers Ltd., part of Springer Nature.",

year = "2018",

month = jul,

day = "26",

doi = "10.1038/s41586-018-0337-2",

language = "English",

volume = "559",

pages = "547--555",

journal = "Nature",

issn = "0028-0836",

publisher = "Nature Research",

number = "7715",

}

TY - JOUR

T1 - Machine learning for molecular and materials science

AU - Butler, Keith T.

AU - Davies, Daniel W.

AU - Cartwright, Hugh

AU - Isayev, Olexandr

AU - Walsh, Aron

PY - 2018/7/26

Y1 - 2018/7/26

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85050673530&partnerID=8YFLogxK

U2 - 10.1038/s41586-018-0337-2

DO - 10.1038/s41586-018-0337-2

M3 - Review article

C2 - 30046072

AN - SCOPUS:85050673530

SN - 0028-0836

VL - 559

SP - 547

EP - 555

JO - Nature

JF - Nature

IS - 7715

ER -

Machine learning for molecular and materials science

Abstract

Bibliographical note

Access to Document

Fingerprint

Cite this