Full-scale ab initio simulations of laser-driven atomistic dynamics

作者：Qiyu Zeng, Bo Chen, Shen Zhang, Dongdong Kang, Han Wang, Xiaoxiang Yu, Jiayu Dai · 发表于：npj Computational Materials · 年份：2023 · DOI：10.1038/s41524-023-01168-4 · 被引用次数：21 · 研究领域：Machine Learning in Materials Science、Spectroscopy and Quantum Chemical Studies、High-pressure geophysics and materials

Abstract The coupling of excited states and ionic dynamics is the basic and challenging point for the materials response at extreme conditions. In the laboratory, the intense laser produces transient nature and complexity with highly nonequilibrium states, making it extremely difficult and interesting for both experimental measurements and theoretical methods. With the inclusion of laser-excited states, we extend an ab initio method into the direct simulations of whole laser-driven microscopic dynamics from solid to liquid. We construct the framework of combining the electron-temperature-dependent deep neural-network potential energy surface with a hybrid atomistic-continuum approach, controlling non-adiabatic energy exchange and atomistic dynamics, which enables consistent interpretation of experimental data. By large-scale ab initio simulations, we demonstrate that the nonthermal effects introduced by hot electrons play a dominant role in modulating the lattice dynamics, thermodynamic pathway, and structural transformation. We highlight that the present work provides a path to realistic computational studies of laser-driven processes, thus bridging the gap between experiments and simulations.