Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter

Chen Tao; Liu Qianrui; Liu Yu; Sun Liang; Chen Mohan

doi:10.1063/5.0163303

Volume 9 Issue 1

Jan. 2024

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Chen Tao, Liu Qianrui, Liu Yu, Sun Liang, Chen Mohan. Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter[J]. Matter and Radiation at Extremes, 2024, 9(1): 015604. doi: 10.1063/5.0163303

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Chen Tao, Liu Qianrui, Liu Yu, Sun Liang, Chen Mohan. Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter[J]. Matter and Radiation at Extremes, 2024, 9(1): 015604. doi: 10.1063/5.0163303

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Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter

doi: 10.1063/5.0163303

HEDPS, CAPT, College of Engineering and School of Physics, Peking University, Beijing 100871, People’s Republic of China

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Corresponding author: a)Author to whom correspondence should be addressed: mohanchen@pku.edu.cn
Received Date: 2023-06-16
Accepted Date: 2023-12-13

Available Online: 2024-01-01

Publish Date: 2024-01-01

Abstract

Abstract

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 temperatures. However, stochastic density functional theory (SDFT) can overcome this limitation. Recently, 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 [Q. Liu and M. Chen, Phys. Rev. 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 eV. Importantly, we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories. Subsequently, we compute and analyze the structural properties, dynamic properties, and transport coefficients of warm dense matter.

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