Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929225677668352 |
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| author | Chen, Tao Liu, Qianrui Liu, Yu Sun, Liang Chen, Mohan |
| author_facet | Chen, Tao Liu, Qianrui Liu, Yu Sun, Liang Chen, Mohan |
| contents | In traditional finite-temperature Kohn-Sham density functional theory (KSDFT), the well-known orbitals wall restricts the use of first-principles molecular dynamics methods at extremely high temperatures. However, stochastic density functional theory (SDFT) can overcome the limitation. Recently, SDFT and its related mixed stochastic-deterministic density functional theory, based on the plane-wave basis set, have been implemented in the first-principles electronic structure software ABACUS [Phys. Rev. B 106, 125132(2022)]. In this study, we combine SDFT with the Born-Oppenheimer molecular dynamics (BOMD) 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. Consequently, we compute and analyze the structural properties, dynamic properties, and transport coefficients of warm dense matter. The abovementioned methods offer a new approach with first-principles accuracy to tackle various properties of warm dense matter. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2306_01637 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | 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 Plasma Physics In traditional finite-temperature Kohn-Sham density functional theory (KSDFT), the well-known orbitals wall restricts the use of first-principles molecular dynamics methods at extremely high temperatures. However, stochastic density functional theory (SDFT) can overcome the limitation. Recently, SDFT and its related mixed stochastic-deterministic density functional theory, based on the plane-wave basis set, have been implemented in the first-principles electronic structure software ABACUS [Phys. Rev. B 106, 125132(2022)]. In this study, we combine SDFT with the Born-Oppenheimer molecular dynamics (BOMD) 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. Consequently, we compute and analyze the structural properties, dynamic properties, and transport coefficients of warm dense matter. The abovementioned methods offer a new approach with first-principles accuracy to tackle various properties of warm dense matter. |
| title | Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2306.01637 |