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Predicting Molecular Properties through Machine Learned Energy Functionals (Johannes Margraf)
Автор: Francesco Sottile
Загружено: 2022-01-13
Просмотров: 209
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In this talk I will present recent work on machine learned energy functionals, which can be used to predict energies, forces and molecular properties on an equal footing. On one hand, this concept is realized in a coarse grained scheme yielding an accurate and effcient charge equilibration method. On the other hand, ML-based density functional approximations working with the full electron density will be presented.
This talk is part of the Discussion meeting on Machine Learning (http://gdr-rest.polytechnique.fr/Disc..., organised by the GDR REST.
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