| Texto completo | |
| Autor(es): |
Focassio, Bruno
;
Domina, Michelangelo
;
Patil, Urvesh
;
Fazzio, Adalberto
;
Sanvito, Stefano
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | NPJ COMPUTATIONAL MATERIALS; v. 9, n. 1, p. 10-pg., 2023-05-29. |
| Resumo | |
Kohn-Sham density functional theory (KS-DFT) is a powerful method to obtain key materials' properties, but the iterative solution of the KS equations is a numerically intensive task, which limits its application to complex systems. To address this issue, machine learning (ML) models can be used as surrogates to find the ground-state charge density and reduce the computational overheads. We develop a grid-centred structural representation, based on Jacobi and Legendre polynomials combined with a linear regression, to accurately learn the converged DFT charge density. This integrates into a ML pipeline that can return any density-dependent observable, including energy and forces, at the quality of a converged DFT calculation, but at a fraction of the computational cost. Fast scanning of energy landscapes and producing starting densities for the DFT self-consistent cycle are among the applications of our scheme. (AU) | |
| Processo FAPESP: | 19/04527-0 - Interface entre isolantes topológicos cristalinos e materiais 2D-trivial: estudo de proximidade via defeitos |
| Beneficiário: | Bruno Focassio |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado Direto |
| Processo FAPESP: | 17/02317-2 - Interfaces em materiais: propriedades eletrônicas, magnéticas, estruturais e de transporte |
| Beneficiário: | Adalberto Fazzio |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 21/12204-6 - Aprendizado de máquina para propriedades de materiais bidimensionais |
| Beneficiário: | Bruno Focassio |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado Direto |