Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science-Reference-Cited by-同舟云学术

Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science

Published:2022 Issue:38 Volume:24 Page:23152-23163
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Baima Jacopo¹^ORCID,Goryaeva Alexandra M.¹^ORCID,Swinburne Thomas D.²,Maillet Jean-Bernard³,Nastar Maylise¹,Marinica Mihai-Cosmin¹^ORCID

Affiliation:

1. Université Paris-Saclay, CEA, Service de Recherches de Métallurgie Physique, Gif-sur-Yvette 91191, France

2. Aix-Marseille Université, CNRS, CINaM UMR 7325, Campus de Luminy, 13288 Marseille, France

3. Université Paris-Saclay, CEA, LMCE, 91680 Bruyères-le-Châtel, France

Abstract

We explore the performance and applicability range of AutoEncoder neural networks, coupled with Adaptive Biasing Force, in computing free energy barriers at finite temperature for defect processes in materials.

Funder

Euratom Research and Training Programme

Agence Nationale de la Recherche

Commissariat à l'Énergie Atomique et aux Énergies Alternatives

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CP/D2CP01917E

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