A Differentiable Neural-Network Force Field for Ionic Liquids
| Autor: | Hadrián Montes-Campos, Jesús Carrete, Sebastian Bichelmaier, Luis M. Varela, Georg K. H. Madsen |
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| Rok vydání: | 2021 |
| Předmět: |
Chemical Physics (physics.chem-ph)
General Chemical Engineering FOS: Physical sciences Ionic Liquids General Chemistry Library and Information Sciences Computational Physics (physics.comp-ph) Article Computer Science Applications Physics - Chemical Physics Quantum Theory Thermodynamics Neural Networks Computer Physics - Computational Physics |
| Zdroj: | Journal of Chemical Information and Modeling |
| ISSN: | 1549-960X |
| Popis: | We present NeuralIL, a model for the potential energy of an ionic liquid that accurately reproduces first-principles results with orders-of-magnitude savings in computational cost. Built on the basis of a multilayer perceptron and spherical Bessel descriptors of the atomic environments, NeuralIL is implemented in such a way as to be fully automatically differentiable. It can thus be trained on ab initio forces instead of just energies, to make the most out of the available data, and can efficiently predict arbitrary derivatives of the potential energy. Using ethylammonium nitrate as the test system, we obtain out-of-sample accuracies better than 2 meV atom–1 ( |
| Databáze: | OpenAIRE |
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