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Condensed Matter > Strongly Correlated Electrons

arXiv:2209.06095 (cond-mat)
[Submitted on 13 Sep 2022 (v1), last revised 25 Sep 2023 (this version, v2)]

Title:Deep Variational Free Energy Approach to Dense Hydrogen

Authors:Hao Xie, Zi-Hang Li, Han Wang, Linfeng Zhang, Lei Wang
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Abstract:We developed a deep generative model-based variational free energy approach to the equations of state of dense hydrogen. We employ a normalizing flow network to model the proton Boltzmann distribution and a fermionic neural network to model the electron wave function at given proton positions. By jointly optimizing the two neural networks we reached a comparable variational free energy to the previous coupled electron-ion Monte Carlo calculation. The predicted equation of state of dense hydrogen under planetary conditions is denser than the findings of ab initio molecular dynamics calculation and empirical chemical model. Moreover, direct access to the entropy and free energy of dense hydrogen opens new opportunities in planetary modeling and high-pressure physics research.
Comments: 7+5 pages, 3+4 figures, code: this https URL
Subjects: Strongly Correlated Electrons (cond-mat.str-el); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
Cite as: arXiv:2209.06095 [cond-mat.str-el]
  (or arXiv:2209.06095v2 [cond-mat.str-el] for this version)
  https://doi.org/10.48550/arXiv.2209.06095
Journal reference: Phys. Rev. Lett. 131, 126501 (2023)
Related DOI: https://doi.org/10.1103/PhysRevLett.131.126501

Submission history

From: Hao Xie [view email]
[v1] Tue, 13 Sep 2022 15:47:21 UTC (1,041 KB)
[v2] Mon, 25 Sep 2023 04:35:33 UTC (5,040 KB)
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