7 citations · 9 across the 3 of their papers we have counts for
3 papers
physics.atom-ph2023★ 2 cited
Calculate electronic excited states using neural networks with effective core potential
JinDe Liu, Chenglong Qin, Xi He +1
The essence of atomic structure theory, quantum chemistry, and computational materials science is solving the multi-electron stationary Schrödinger equation. The Quantum Monte Carl…
cond-mat.mtrl-sci2023★ 7 cited
Enhancing crystal structure prediction by combining computational and experimental data via graph networks
Chenglong Qin, Jinde Liu, Shiyin Ma +3
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 r…
physics.comp-ph2022
Solving Multi-Dimensional Schrödinger Equations Based on EPINNs
Jinde Liu, Chen Yang, Gang Jiang
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 eige…