Výsledky vyhledávání

Report

Calculate electronic excited states using neural networks with effective core potential

Autor: Liu, JinDe, Qin, Chenglong, He, Xi, Jiang, Gang

The essence of atomic structure theory, quantum chemistry, and computational materials science is solving the multi-electron stationary Schr\"odinger equation. The Quantum Monte Carlo-based neural network wave function method has surpassed traditiona

Externí odkaz: http://arxiv.org/abs/2312.15216

Zobrazit plný text záznamu

Report

Enhancing crystal structure prediction by combining computational and experimental data via graph networks

Autor: Qin, Chenglong, Liu, Jinde, Ma, Shiyin, Du, Jiguang, Jiang, Gang, Zhao, Liang

Publikováno v: J. Mater. Chem. A, 2024, Advance Article

Crystal structure prediction (CSP) stands as a powerful tool in materials science, driving the discovery and design of innovative materials. However, existing CSP methods heavily rely on formation enthalpies derived from density functional theory (DF

Externí odkaz: http://arxiv.org/abs/2311.11665

Zobrazit plný text záznamu

Report

Solving Multi-Dimensional Schr\'{o}dinger Equations Based on EPINNs

Autor: Liu, Jinde, Yang, Chen, Jiang, Gang

Due to the good performance of neural networks in high-dimensional and nonlinear problems, machine learning is replacing traditional methods and becoming a better approach for eigenvalue and wave function solutions of multi-dimensional Schr\"{o}dinge

Externí odkaz: http://arxiv.org/abs/2210.00454

Zobrazit plný text záznamu