GAMaterial-A genetic-algorithm software for material design and discovery.
atomic clusters
cluster interfaces
defects
genetic algorithm
global optimization
machine learning
Journal
Journal of computational chemistry
ISSN: 1096-987X
Titre abrégé: J Comput Chem
Pays: United States
ID NLM: 9878362
Informations de publication
Date de publication:
15 Mar 2023
15 Mar 2023
Historique:
revised:
26
09
2022
received:
11
05
2022
accepted:
06
11
2022
pubmed:
30
11
2022
medline:
30
11
2022
entrez:
29
11
2022
Statut:
ppublish
Résumé
Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
814-823Subventions
Organisme : Fundação de Amparo à Pesquisa do Espírito Santo (FAPES) - project
ID : CNPq/FAPES PPP 22/2018
Organisme : Conselho Nacional para o Desenvolvimento Científico e Tecnológico (CNPq)
Organisme : Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior (CAPES)
Organisme : Consejo Nacional de Ciencia y Tecnología, Mexico (CONACYT)
ID : A1-S-11929
Organisme : National Research Council of Canada
Organisme : Artificial Intelligence for Design program
Organisme : Natural Sciences and Engineering Research Council of Canada
ID : RGPIN-2019-03976
Informations de copyright
© 2022 Wiley Periodicals LLC.
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