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Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces
The goal of many computational physicists and chemists is the ability to bridge the gap between atomistic length scales of about a few multiples of an Ångström (Å), i. e., 10(−10) m, and meso- or macroscopic length scales by virtue of simulations. The same applies to timescales. Machine learning tec...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793815/ https://www.ncbi.nlm.nih.gov/pubmed/33425857 http://dx.doi.org/10.3389/fchem.2020.601029 |