Retrospective on a decade of machine learning for chemical discovery
| Autor: | O. Anatole von Lilienfeld, Kieron Burke |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Science General Physics and Astronomy 02 engineering and technology Machine learning computer.software_genre General Biochemistry Genetics and Molecular Biology Field (computer science) 03 medical and health sciences lcsh:Science Multidisciplinary business.industry Comment Method development General Chemistry 021001 nanoscience & nanotechnology Materials science 030104 developmental biology Atomistic models lcsh:Q Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | Nature Communications, Vol 11, Iss 1, Pp 1-4 (2020) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | Standfirst Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order. |
| Databáze: | OpenAIRE |
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