期刊
NATURE
卷 559, 期 7715, 页码 547-555出版社
NATURE PORTFOLIO
DOI: 10.1038/s41586-018-0337-2
关键词
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资金
- EPSRC [EP/M009580/1, EP/K016288/1, EP/L016354/1]
- Royal Society
- Leverhulme Trust
- DOD-ONR [N00014-16-1-2311]
- Eshelman Institute for Innovation award
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.
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