5 papers · 1 filter
Nonadiabatic Molecular Dynamics on Real-time Excited-State Surfaces via Machine Learning Hamiltonians
Changwei Zhang, Yang Zhong, Zhi-Guo Tao +6
Simulating the coupled, nonequilibrium dynamics of electrons and nuclei is a central challenge in chemistry, physics, and materials science, governing phenomena from photocatalysis…
Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians
Yang Zhong, Xiwen Li, Xingao Gong +1
Machine-learning electronic Hamiltonians achieve orders-of-magnitude speedups over density-functional theory, yet current models omit long-range Coulomb interactions that govern ph…
Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks
Rui Wang, Hongyu Yu, Yang Zhong +1
The application of machine learning methodologies for predicting properties within materials science has garnered significant attention. Among recent advancements, Kolmogorov-Arnol…
Advancing Nonadiabatic Molecular Dynamics Simulations for Solids: Achieving Supreme Accuracy and Efficiency with Machine Learning
Changwei Zhang, Yang Zhong, Zhi-Guo Tao +7
Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framewo…
Universal Machine Learning Kohn-Sham Hamiltonian for Materials
Yang Zhong, Hongyu Yu, Jihui Yang +3
While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persi…