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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Journal of computational physics Jg. 418; S. 109633
Hauptverfasser:	Grogan, Francesca, Lei, Huan, Li, Xiantao, Baker, Nathan A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge Elsevier Inc 01.10.2020 Elsevier Science Ltd
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!