Accurate and thermodynamically consistent hydrogen equation of state for planetary modeling with flow matching
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Abstract (arXiv)
Accurate determination of the equation of state of dense hydrogen is essential for understanding gas giants. Currently, there is still no consensus on methods for calculating its entropy, which play a fundamental role and can result in qualitatively different predictions for Jupiter's interior. Here, we investigate various aspects of entropy calculation for dense hydrogen based on ab initio molecular dynamics simulations. Specifically, we employ the recently developed flow matching method to validate the accuracy of the traditional thermodynamic integration approach. We then clearly identify pitfalls in previous attempts and propose a reliable framework for constructing the hydrogen equation of state, which is accurate and thermodynamically consistent across a wide range of temperature and pressure conditions. This allows us to conclusively address the long-standing discrepancies in Jupiter's adiabat among earlier studies, demonstrating the potential of our approach for providing reliable equations of state of diverse materials.
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References · 20
- 2017Attention Is All You Needcited by 60,361
- 2022Flow Matching for Generative Modelingcited by 5,702
- 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
- 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
- 2022Building Normalizing Flows with Stochastic Interpolantscited by 995
- 2021E(n) Equivariant Graph Neural Networkscited by 965
- 2023Flow Matching on General Geometriescited by 265
- 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 200
- 2023Equivariant flow matchingcited by 135
- 2018Solving Statistical Mechanics Using Variational Autoregressive Networkscited by 100
- 2020Targeted free energy estimation via learned mappingscited by 67
- 2019Equivariant Flows: sampling configurations for multi-body systems with symmetric energiescited by 57
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- 2018Neural Network Renormalization Groupcited by 48
- 2021Normalizing flows for atomic solidscited by 39
- 2018Monge-Amp`ere Flow for Generative Modelingcited by 33
- 2022Discovering Quantum Phase Transitions with Fermionic Neural Networkscited by 26
- 2021Ab-initio study of interacting fermions at finite temperature with neural canonical transformationcited by 16
- 2022m^ast of two-dimensional electron gas: a neural canonical transformation studycited by 7
- 2022Deep Variational Free Energy Approach to Dense Hydrogencited by 7
- 2024Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracycited by 2
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