Machine-learned approximations to Density Functional Theory Hamiltonians

Large scale Density Functional Theory (DFT) based electronic structure calculations are highly time consuming and scale poorly with system size. While semi-empirical approximations to DFT result in a reduction in computational time versus ab initio DFT, creating such approximations involves signific...

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Veröffentlicht in:	Scientific reports 2017-02, Vol.7 (1), p.42669-42669, Article 42669
Hauptverfasser:	Hegde, Ganesh, Bowen, R. Chris
Format:	Artikel
Sprache:	eng
Schlagworte:	639/301/1034/1037 639/705/1042 Approximation Automation Boundary conditions Case studies Computer applications Datasets Humanities and Social Sciences Learning algorithms Machine learning multidisciplinary Neighborhoods Science
Online-Zugang:	Volltext
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