Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter
作者机构：HEDPSCAPTCollege of Engineering and School of PhysicsPeking UniversityBeijing 100871People’s Republic of China
出 版 物：《Matter and Radiation at Extremes》 (极端条件下的物质与辐射（英文）)
年 卷 期：2024年第9卷第1期
页 面：44-57页
核心收录：
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
基 金：supported by the National Natural Science Foundation of China under Grant Nos.12122401 and 12074007
主 题：stochastic theory functional
摘 要：In traditional finite-temperature Kohn–Sham density functional theory(KSDFT),the partial occupation of a large number of high-energy KS eigenstates restricts the use of first-principles molecular dynamics methods at extremely high ***,stochastic density functional theory(SDFT)can overcome this ***,SDFT and the related mixed stochastic–deterministic density functional theory,based on a plane-wave basis set,have been implemented in the first-principles electronic structure software ABACUS[*** and ***,***.B 106,125132(2022)].In this study,we combine SDFT with the Born–Oppenheimer molecular dynamics method to investigate systems with temperatures ranging from a few tens of eV to 1000 ***,we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long ***,we compute and analyze the structural properties,dynamic properties,and transport coefficients of warm dense matter.
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