Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10484
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Main Title: Exploring density functional subspaces with genetic algorithms
Author(s): Gastegger, Michael
González, Leticia
Marquetand, Philipp
Type: Article
Language Code: en
Abstract: We use a genetic algorithm to explore the subspace of combination and parametrization patterns spanned by a set of popular exchange and correlation functional approximations. Using the well-balanced GMTKN30 benchmark database to guide the evolutionary process, we find that the genetic algorithm is able to recover variants of several popular generalized gradient approximation functionals and hybrid functionals. For the latter class, the algorithm is able to identify a reparametrized version of the three-parameter hybrid B3PW91, which shows significantly improved performance compared to conventional versions of B3PW91. Furthermore, the possible application of this algorithm to automatically construct so-called “niche”-functionals—specially tailored to specific applications—is demonstrated.
URI: https://depositonce.tu-berlin.de/handle/11303/11595
http://dx.doi.org/10.14279/depositonce-10484
Issue Date: 14-Dec-2018
Date Available: 25-Aug-2020
DDC Class: 540 Chemie und zugeordnete Wissenschaften
Subject(s): genetic algorithm
density functional theory
computational chemistry
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Monatshefte für Chemie / Chemical Monthly
Publisher: Springer
Publisher Place: Wien
Volume: 150
Publisher DOI: 10.1007/s00706-018-2335-3
Page Start: 173
Page End: 182
EISSN: 1434-4475
ISSN: 0026-9247
Appears in Collections:FG Maschinelles Lernen » Publications

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