INCT participa de nova publicação sobre nova abordagem para soluções de Equações de KS na Nature Computational Materials
O coordenador do INCT – Materials Informatics, Adalberto Fazzio, fez parte de uma recente publicação na revista Nature – Computational Materials em que apresentam uma nova abordagem para alcançar soluções de Equações de Kohn-Sham.
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.
