Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians
Abstract
Abstract 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 framework, N2AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. Distinct from conventional machine learning methods that predict key quantities in NAMD, N2AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N2AMD not only achieves impressive efficiency in performing NAMD simulations at the hybrid functional level within the framework of the classical path approximation (CPA), but also demonstrates great potential in predicting non-adiabatic coupling vectors and suggests a method to go beyond CPA. Furthermore, N2AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework offers a reliable and efficient approach for conducting accurate NAMD simulations across various condensed materials.
Article Details
Authors (10)
Changwei Zhang
Key Laboratory for Computational Physical Sciences (MOE), State Key Laboratory of Surface Physics, Institute of Computational Physical Sciences and Department of Physics
Yang Zhong
College of Chemistry and Chemical Engineering/Film Energy Chemistry for Jiangxi Provincial Key Laboratory (FEC), Nanchang University, Nanchang, China.
Zhi-Guo Tao
Key Laboratory for Computational Physical Sciences (MOE), State Key Laboratory of Surface Physics, Institute of Computational Physical Sciences and Department of Physics
Xinming Qin
Hefei National Research Center for Physical Sciences at the Microscale, and Hefei National Laboratory, University of Science and Technology of China 1 , Hefei, Anhui 230026,
Honghui Shang
Zhenggang Lan
MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University 3 , Guangzhou 510006,
Oleg V. Prezhdo
Department of Chemistry and Chemical Biology
Xin-Gao Gong
Key Laboratory for Computational Physical Sciences (MOE), State Key Laboratory of Surface Physics, Institute of Computational Physical Sciences and Department of Physics
Weibin Chu
Key Laboratory for Computational Physical Sciences (MOE), State Key Laboratory of Surface Physics, Institute of Computational Physical Sciences and Department of Physics
Hongjun Xiang
Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan University 24 , Shanghai 200433,