Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al

We propose a machine-learning interatomic potential for multi-component magnetic materials. In this potential we consider magnetic moments as degrees of freedom (features) along with atomic positions, atomic types, and lattice vectors. We create a training set with constrained DFT (cDFT) that allows...

Descripción completa

Detalles Bibliográficos
Autores principales: Kotykhov, Alexey S., Gubaev, Konstantin, Hodapp, Max, Tantardini, Christian, Shapeev, Alexander V., Novikov, Ivan S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643701/
https://www.ncbi.nlm.nih.gov/pubmed/37957211
http://dx.doi.org/10.1038/s41598-023-46951-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643701/
https://www.ncbi.nlm.nih.gov/pubmed/37957211
http://dx.doi.org/10.1038/s41598-023-46951-x

Ejemplares similares