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The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near ab initio accuracy at low cost. However a data-driven approach is naturally inefficient fo...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Royal Society of Chemistry
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5897848/ https://www.ncbi.nlm.nih.gov/pubmed/29719699 http://dx.doi.org/10.1039/c7sc04934j |