Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning
Abstract
Nonadiabatic coupled electron-nuclear dynamics upon electronic excitation underpin the microscopic mechanism and rational modulation of diverse photoinduced functional phenomena in materials, yet their direct first-principles simulations remain computationally demanding. Here we develop a framework for nonadiabatic excited-state machine-learning molecular dynamics (EMLMD) simulations, where the nonequilibrium electronic information upon photoexcitation such as electron temperature is rigorously calibrated from high-precision real-time time-dependent density functional theory (rt-TDDFT) benchmark simulations, enabling accurate reconstruction of excited-state potential energy surfaces (PES). This framework natively incorporates the excited-state electron-phonon couplings and intrinsically captures photoinduced phonon anharmonicity, both of which are missing in standard machine learning molecular dynamics, thus delivering first-principles-level accuracy for excited-state atomic evolutions. Large-scale EMLMD simulations resolve time- and momentum-resolved phonon dynamics in photoexcited materials, directly uncovering the competition between photogenerated coherent phonons and thermal phonons during photoinduced phase transition of bismuth. It also simultaneously resolves elusive atomic-scale microscopic dynamics and global structural rearrangement for selenium photoamorphization. Balancing high accuracy and efficiency, EMLMD offers a versatile paradigm to tackle key challenges in the study of complex excited-state molecular dynamics.
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BibTeX
@misc{jia2026accelerating,
title = {{Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning}},
author = {Yunzhe Jia and Fankai Xie and Yunfei Bai and Miao Liu and Cui Zhang and Sheng Meng},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01492},
eprint = {2609.01492},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.01492},
}
RIS
TY - UNPB
TI - Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning
AU - Yunzhe Jia
AU - Fankai Xie
AU - Yunfei Bai
AU - Miao Liu
AU - Cui Zhang
AU - Sheng Meng
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.01492
UR - https://arxiv.org/abs/2609.01492
AB - Nonadiabatic coupled electron-nuclear dynamics upon electronic excitation underpin the microscopic mechanism and rational modulation of diverse photoinduced functional phenomena in materials, yet their direct first-principles simulations remain computationally demanding. Here we develop a framework for nonadiabatic excited-state machine-learning molecular dynamics (EMLMD) simulations, where the nonequilibrium electronic information upon photoexcitation such as electron temperature is rigorously calibrated from high-precision real-time time-dependent density functional theory (rt-TDDFT) benchmark simulations, enabling accurate reconstruction of excited-state potential energy surfaces (PES). This framework natively incorporates the excited-state electron-phonon couplings and intrinsically captures photoinduced phonon anharmonicity, both of which are missing in standard machine learning molecular dynamics, thus delivering first-principles-level accuracy for excited-state atomic evolutions. Large-scale EMLMD simulations resolve time- and momentum-resolved phonon dynamics in photoexcited materials, directly uncovering the competition between photogenerated coherent phonons and thermal phonons during photoinduced phase transition of bismuth. It also simultaneously resolves elusive atomic-scale microscopic dynamics and global structural rearrangement for selenium photoamorphization. Balancing high accuracy and efficiency, EMLMD offers a versatile paradigm to tackle key challenges in the study of complex excited-state molecular dynamics.
ER -