<div class="csl-bib-body">
<div class="csl-entry">Wanzenböck, R., Buchner, F., Kovács, P., Madsen, G. K. H., & Carrete, J. (2024). Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches. <i>Computer Physics Communications</i>, <i>297</i>, Article 109065. https://doi.org/10.1016/j.cpc.2023.109065</div>
</div>
-
dc.identifier.issn
0010-4655
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/191692
-
dc.description.abstract
Clinamen2 is a versatile functional-style Python implementation of the covariance matrix adaptation evolution strategy (CMA-ES) utilizing Cholesky decomposition. On top of a problem-agnostic core algorithm, the software package offers a suite of utilities and library code enabling applications to important atomistic structure searches. Features include massively distributed computation and the BI-Population restart scheme. This article details the general code structure and introduces examples that illustrate some relevant applications for the materials science and chemistry worlds, including interfacing to density-functional-theory codes and machine-learned surrogate models. The functional design renders the code modular and adaptable, and makes the creation of interfaces to other atomistic software straightforward.
en
dc.language.iso
en
-
dc.publisher
ELSEVIER
-
dc.relation.ispartof
Computer Physics Communications
-
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
-
dc.subject
Python
en
dc.subject
Covariance matrix
en
dc.subject
density-functional-theory
en
dc.title
Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches
en
dc.type
Article
en
dc.type
Artikel
de
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
en
dc.rights.license
Creative Commons Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International