@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 = "Funding Information: Acknowledgements This work was supported by the EPSRC (grant numbers EP/M009580/1, EP/K016288/1 and EP/L016354/1), the Royal Society and the Leverhulme Trust. O.I. acknowledges support from DOD-ONR (N00014-16-1-2311) and an Eshelman Institute for Innovation award. 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 Publishing Group",
number = "7715",
}