Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386977013> ?p ?o ?g. }
- W4386977013 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." @default.
- W4386977013 created "2023-09-23" @default.
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- W4386977013 date "2023-09-22" @default.
- W4386977013 modified "2023-10-17" @default.
- W4386977013 title "Deep Variational Free Energy Approach to Dense Hydrogen" @default.
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- W4386977013 doi "https://doi.org/10.1103/physrevlett.131.126501" @default.
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- W4386977013 hasPublicationYear "2023" @default.
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