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 Chemistry

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

2732 Citations (SciVal)

1047 Downloads (Pure)

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
Early online date	25 Jul 2018
DOIs	https://doi.org/10.1038/s41586-018-0337-2
Publication status	Published - 26 Jul 2018

ASJC Scopus subject areas

General

Access to Document

10.1038/s41586-018-0337-2

Walsh_MachineLearning_FinalSubmission
This is the Authors' Accepted Manuscript of an article published in Butler, KT, Davies, DW, Cartwright, H, Isayev, O & Walsh, A 2018, 'Machine learning for molecular and materials science' Nature, vol. 559, no. 7715, pp. 547-555 and available via: http://dx.doi.org/10.1038/s41586-018-0337-2
Accepted author manuscript, 314 KBLicence: Unspecified

Cite this

@article{03379e8b3fe748139d45b94c61ad9740,

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",

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

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

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Multi-Scale Modelling of Hybrid Perovskites for Solar Cells

Cite this

Machine learning for molecular and materials science

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Projects

Multi-Scale Modelling of Hybrid Perovskites for Solar Cells

Cite this