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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-ph2023★ 1 cited
TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors
Xin Chen, Yucheng Ouyang, Zhenchuan Chen +7
Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extre…