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• W2898846720 abstract "We use machine learning to enable large-scale molecular dynamics (MD) of a correlated electron model under the Gutzwiller approximation scheme. This model exhibits a Mott transition as a function of on-site Coulomb repulsion $U$. The repeated solution of the Gutzwiller self-consistency equations would be prohibitively expensive for large-scale MD simulations. We show that machine learning models of the Gutzwiller potential energy can be remarkably accurate. The models, which are trained with $N=33$ atoms, enable highly accurate MD simulations at much larger scales ($Nensuremath{gtrsim}{10}^{3}$). We investigate the physics of the smooth Mott crossover in the fluid phase." @default.
• W2898846720 created "2018-11-09" @default.
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• W2898846720 date "2019-04-08" @default.
• W2898846720 modified "2023-10-15" @default.
• W2898846720 title "Machine learning for molecular dynamics with strongly correlated electrons" @default.
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• W2898846720 doi "https://doi.org/10.1103/physrevb.99.161107" @default.
• W2898846720 hasPublicationYear "2019" @default.
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