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Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
[Image: see text] Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models can help to be data economic as they can be successfully constrained using the underlying symmetry and conservation laws o...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical Society
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639201/ https://www.ncbi.nlm.nih.gov/pubmed/36279418 http://dx.doi.org/10.1021/acs.jpclett.2c02632 |