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Activation–Relaxation Technique: An efficient way to find minima and saddle points of potential energy surfaces

Authors :
Antoine Jay
Miha Gunde
Nicolas Salles
Matic Poberžnik
Layla Martin-Samos
Nicolas Richard
Stefano de Gironcoli
Normand Mousseau
Anne Hémeryck
Équipe Modélisation Multi-niveaux des Matériaux (LAAS-M3)
Laboratoire d'analyse et d'architecture des systèmes (LAAS)
Université Toulouse Capitole (UT Capitole)
Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse)
Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J)
Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3)
Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP)
Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole)
Université de Toulouse (UT)
CNR Istituto Officina dei Materiali (IOM)
National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR)
CEA DAM ILE-DE-FRANCE - Bruyères-le-Châtel [Arpajon] (CEA DAM IDF)
Scuola Internazionale Superiore di Studi Avanzati / International School for Advanced Studies (SISSA / ISAS)
Université de Montréal (UdeM)
Source :
Computational Materials Science, Computational Materials Science, 2022, 209, pp.111363. ⟨10.1016/j.commatsci.2022.111363⟩
Publication Year :
2022
Publisher :
HAL CCSD, 2022.

Abstract

International audience; The Activation–Relaxation Technique (ARTn) is an efficient technique for finding the minima and saddle points of multidimensional functions such as the potential energy surface of atomic systems in chemistry. In this work we detail and illustrate significant improvements made to the algorithm, regarding both preprocessing and the activation process itself. As showcased, these advances significantly reduce ARTn computational costs, especially when applied with ab initio description. With these modifications, ARTn establishes itself as a very efficient method for exploring the energy landscape and chemical reactions associated with complex mechanisms.

Details

Language :
English
ISSN :
09270256
Database :
OpenAIRE
Journal :
Computational Materials Science, Computational Materials Science, 2022, 209, pp.111363. ⟨10.1016/j.commatsci.2022.111363⟩
Accession number :
edsair.doi.dedup.....38fee38d8348661ce7c739f19b6c1b94
Full Text :
https://doi.org/10.1016/j.commatsci.2022.111363⟩