52 citations · 135 across the 21 of their papers we have counts for
Showing 2024 · physics.comp-phShow all
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physics.comp-ph2024★ 1 cited
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…
physics.comp-ph2024★ 3 cited
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…
physics.comp-ph2024★ 42 cited
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…