Data-driven molecular modeling with the generalized Langevin equation

•Data-driven GLE approximation balances computational cost and accuracy.•Accuracy tunable by adjusting order of memory kernel approximation.•Approximate GLE predicts non-equilibrium properties, like autocorrelation, well. The complexity of molecular dynamics simulations necessitates dimension reduct...

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Vydané v:	Journal of computational physics Ročník 418; s. 109633
Hlavní autori:	Grogan, Francesca, Lei, Huan, Li, Xiantao, Baker, Nathan A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cambridge Elsevier Inc 01.10.2020 Elsevier Science Ltd
Predmet:	Approximation Coarse-grained models Complexity Computational physics Data-driven parametrization Dimension reduction Generalized Langevin equation Granulation Mathematical analysis Molecular dynamics Molecular dynamics Coarse-grained models Dimension reduction Generalized Langevin equation Data-driven parametrization
ISSN:	0021-9991, 1090-2716
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