| Full text | |
| Author(s): |
Focassio, Bruno
;
Domina, Michelangelo
;
Patil, Urvesh
;
Fazzio, Adalberto
;
Sanvito, Stefano
Total Authors: 5
|
| Document type: | Journal article |
| Source: | NPJ COMPUTATIONAL MATERIALS; v. 9, n. 1, p. 10-pg., 2023-05-29. |
| Abstract | |
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) | |
| FAPESP's process: | 19/04527-0 - Interface between crystalline topological insulators and 2D-trivial materials: defect proximity study |
| Grantee: | Bruno Focassio |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |
| FAPESP's process: | 17/02317-2 - Interfaces in materials: electronic, magnetic, structural and transport properties |
| Grantee: | Adalberto Fazzio |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 21/12204-6 - Machine learning for two-dimensional materials' properties |
| Grantee: | Bruno Focassio |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate (Direct) |