Best practices in machine learning for chemistry
Statistical tools based on machine learning are becoming integrated into chemistry research workflows. We discuss the elements necessary to train reliable, repeatable and reproducible models, and recommend a set of guidelines for machine learning reports.
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| Veröffentlicht in: | Nature chemistry 2021-06, Vol.13 (6), p.505-508 |
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| container_title | Nature chemistry |
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| creator | Artrith, Nongnuch Butler, Keith T. Coudert, François-Xavier Han, Seungwu Isayev, Olexandr Jain, Anubhav Walsh, Aron |
| description | Statistical tools based on machine learning are becoming integrated into chemistry research workflows. We discuss the elements necessary to train reliable, repeatable and reproducible models, and recommend a set of guidelines for machine learning reports. |
| doi_str_mv | 10.1038/s41557-021-00716-z |
| format | Article |
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| subjects | 639/638/563/606 639/638/563/980 706/648/479 706/648/697/129 Analytical Chemistry Best practice Biochemistry Chemical Sciences Chemistry Chemistry and Materials Science Chemistry/Food Science Comment Computer Science Inorganic Chemistry Learning algorithms Machine Learning or physical chemistry Organic Chemistry Physical Chemistry Reproducibility Theoretical and |
| title | Best practices in machine learning for chemistry |
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