Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics

Wang X, Hamel S, Cheng B. 2026. Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics. Nature Communications. 17, 10382.

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Corresponding author has ISTA affiliation

Abstract
The location of the hydrogen-helium (H/He) immiscibility boundary controls whether and where helium rain occurs in giant planets, yet it remains uncertain because high-pressure experiments are challenging and ab initio simulations are limited in system size and simulation time. We map this boundary by computing composition-dependent chemical potentials from large-scale molecular dynamics driven by machine learning potentials trained on three density functional approximations (PBE, vdW-DF, and the hybrid HSE). Across the pressure range of 100-1000 GPa, the demixing temperatures are typically ~ 2000 K lower than previous ab initio simulations using small system sizes, and this discrepancy is larger than the estimated error from the chosen density functionals, machine learning potentials, neglect of nuclear quantum effects, and statistical uncertainties. Fitting the H/He mixing free energy to a Redlich-Kister regular solution model rationalizes the thermodynamic driving force for phase separation, and provides a predictive representation of the boundary. Comparing with current planetary interior profiles indicates that helium rain is plausible in Saturn but unlikely in the warmer interior of Jupiter. Our results narrow the uncertainty in the H/He immiscibility boundary and provide inputs for planetary models that couple demixing, heat transport, and composition gradients in gas giants.
Publishing Year
Date Published
2026-09-01
Journal Title
Nature Communications
Publisher
Springer Nature
Acknowledgement
The authors are thankful to Armin Bergermann and Ronald Redmer for generously sharing AIMD trajectories and data, and for valuable discussions and feedback on the manuscript. The authors also acknowledge the research computing facilities provided by the high-performance computing (HPC) cluster at the Institute of Science and Technology Austria (ISTA), Berkeley Research Computing (BRC) at the University of California, Berkeley (UCB), and Lawrence Livermore National Laboratory (LLNL). B.C. was supported by the Laboratory Directed Research and Development (LDRD) Program at Lawrence Berkeley National Laboratory (LBNL) under project ID 110990-001. Part of this work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. S.H. was supported by the LDRD Program at LLNL under project tracking code 23-SI-006.
Acknowledged SSUs
Volume
17
Article Number
10382
eISSN
IST-REx-ID

Cite this

Wang X, Hamel S, Cheng B. Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics. Nature Communications. 2026;17. doi:10.1038/s41467-026-77117-8
Wang, X., Hamel, S., & Cheng, B. (2026). Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-026-77117-8
Wang, Xiaoyu, Sebastien Hamel, and Bingqing Cheng. “Hydrogen-Helium Immiscibility Boundary in Gas-Giant Planetary Interiors from Machine-Learning Molecular Dynamics.” Nature Communications. Springer Nature, 2026. https://doi.org/10.1038/s41467-026-77117-8.
X. Wang, S. Hamel, and B. Cheng, “Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics,” Nature Communications, vol. 17. Springer Nature, 2026.
Wang X, Hamel S, Cheng B. 2026. Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics. Nature Communications. 17, 10382.
Wang, Xiaoyu, et al. “Hydrogen-Helium Immiscibility Boundary in Gas-Giant Planetary Interiors from Machine-Learning Molecular Dynamics.” Nature Communications, vol. 17, 10382, Springer Nature, 2026, doi:10.1038/s41467-026-77117-8.
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