---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '23089'
abstract:
- lang: eng
text: 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.
acknowledged_ssus:
- _id: ScienComp
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.
article_number: '10382'
article_processing_charge: Yes
article_type: original
author:
- first_name: Xiaoyu
full_name: Wang, Xiaoyu
id: 8dff9c62-32b0-11ee-9fa8-fc73025e10f3
last_name: Wang
- first_name: Sebastien
full_name: Hamel, Sebastien
last_name: Hamel
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: 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
apa: 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
chicago: 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.
ieee: 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.
ista: 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.
mla: 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.
short: X. Wang, S. Hamel, B. Cheng, Nature Communications 17 (2026).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: 'The training data, fitted MLPs, MLP molecular dynamics
results, and source data underlying all figures and tables in the main text and
Supplementary Information have been deposited in the Zenodo repository: https://doi.org/10.5281/zenodo.21632034.
The data-analysis and plotting scripts used to generate the figures and tables in
the main text and Supplementary Information are available in the GitHub repository:
https://github.com/ChengUCB/HighPressure_HHe.'
date_created: 2026-10-11T22:02:04Z
date_published: 2026-09-01T00:00:00Z
date_updated: 2026-10-12T11:00:16Z
day: '01'
ddc:
- '540'
department:
- _id: GradSch
- _id: BiCh
doi: 10.1038/s41467-026-77117-8
external_id:
pmid:
- '42816492'
file:
- access_level: open_access
checksum: f39b50a550623a65aea2405b527e068c
content_type: application/pdf
creator: dernst
date_created: 2026-10-12T10:57:42Z
date_updated: 2026-10-12T10:57:42Z
file_id: '23107'
file_name: 2026_NatureComm_Wang.pdf
file_size: 1559219
relation: main_file
success: 1
file_date_updated: 2026-10-12T10:57:42Z
fulldoi: https://doi.org/10.1038/s41467-026-77117-8
has_accepted_license: '1'
intvolume: ' 17'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from
machine-learning molecular dynamics
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2026'
...
---
OA_place: repository
OA_type: green
_id: '20926'
abstract:
- lang: eng
text: Most current machine learning interatomic potentials (MLIPs) rely on short-range
approximations, without explicit treatment of long-range electrostatics. To address
this, we recently developed the Latent Ewald Summation (LES) method, which infers
electrostatic interactions, polarization, and Born effective charges (BECs), just
by learning from energy and force training data. Here, we present LES as a standalone
library, compatible with any short-range MLIP, and demonstrate its integration
with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark
LES-enhanced models on distinct systems, including bulk water, polar dipeptides,
and gold dimer adsorption on defective substrates, and show that LES not only
captures correct electrostatics but also improves accuracy. Additionally, we scale
LES to large and chemically diverse data by training MACELES-OFF on the SPICE
set containing molecules and clusters, making a universal MLIP with electrostatics
for organic systems, including biomolecules. MACELES-OFF is more accurate than
its short-range counterpart (MACE-OFF) trained on the same data set, predicts
dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling
efficient long-range electrostatics without directly training on electrical properties,
LES paves the way for electrostatic foundation MLIPs.
acknowledgement: Research reported in this publication was supported by the National
Institute Of General Medical Sciences of the National Institutes of Health under
Award Number R35GM159986. The content is solely the responsibility of the authors
and does not necessarily represent the official views of the National Institutes
of Health. D.K. and B.C. acknowledge funding from Toyota Research Institute Synthesis
Advanced Research Challenge. T.J.I., D.S.K. and P.Z. acknowledge funding from BIDMaP
Postdoctoral Fellowship. T.J.I. used resources of the National Energy Research Scientific
Computing Center (NERSC), a Department of Energy Office of Science User Facility
using NERSC award DOEERCAP0031751 ′GenAI@NERSC’. The authors thank Bowen Deng for
valuable discussions on MatGL implementation, and thank Gabor Csanyi for stimulating
discussions.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Dongjin
full_name: Kim, Dongjin
last_name: Kim
- first_name: Xiaoyu
full_name: Wang, Xiaoyu
id: 8dff9c62-32b0-11ee-9fa8-fc73025e10f3
last_name: Wang
- first_name: Santiago
full_name: Vargas, Santiago
last_name: Vargas
- first_name: Peichen
full_name: Zhong, Peichen
last_name: Zhong
- first_name: Daniel S.
full_name: King, Daniel S.
last_name: King
- first_name: Theo Jaffrelot
full_name: Inizan, Theo Jaffrelot
last_name: Inizan
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Kim D, Wang X, Vargas S, et al. A universal augmentation framework for long-range
electrostatics in machine learning interatomic potentials. Journal of Chemical
Theory and Computation. 2025;21(24):12709-12724. doi:10.1021/acs.jctc.5c01400
apa: Kim, D., Wang, X., Vargas, S., Zhong, P., King, D. S., Inizan, T. J., &
Cheng, B. (2025). A universal augmentation framework for long-range electrostatics
in machine learning interatomic potentials. Journal of Chemical Theory and
Computation. American Chemical Society. https://doi.org/10.1021/acs.jctc.5c01400
chicago: Kim, Dongjin, Xiaoyu Wang, Santiago Vargas, Peichen Zhong, Daniel S. King,
Theo Jaffrelot Inizan, and Bingqing Cheng. “A Universal Augmentation Framework
for Long-Range Electrostatics in Machine Learning Interatomic Potentials.” Journal
of Chemical Theory and Computation. American Chemical Society, 2025. https://doi.org/10.1021/acs.jctc.5c01400.
ieee: D. Kim et al., “A universal augmentation framework for long-range electrostatics
in machine learning interatomic potentials,” Journal of Chemical Theory and
Computation, vol. 21, no. 24. American Chemical Society, pp. 12709–12724,
2025.
ista: Kim D, Wang X, Vargas S, Zhong P, King DS, Inizan TJ, Cheng B. 2025. A universal
augmentation framework for long-range electrostatics in machine learning interatomic
potentials. Journal of Chemical Theory and Computation. 21(24), 12709–12724.
mla: Kim, Dongjin, et al. “A Universal Augmentation Framework for Long-Range Electrostatics
in Machine Learning Interatomic Potentials.” Journal of Chemical Theory and
Computation, vol. 21, no. 24, American Chemical Society, 2025, pp. 12709–24,
doi:10.1021/acs.jctc.5c01400.
short: D. Kim, X. Wang, S. Vargas, P. Zhong, D.S. King, T.J. Inizan, B. Cheng, Journal
of Chemical Theory and Computation 21 (2025) 12709–12724.
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: The training sets, training scripts, and trained potentials
are available at https://github.com/ChengUCB/les_fit. The LES library is publicly
available at https://github.com/ChengUCB/les. The CACE package with the LES implementation
is available at https://github.com/BingqingCheng/cace. The MACE package with the
LES implementation is available at https://github.com/ACEsuit/mace. The NequIP and
Allegro LES extension package is available at https://github.com/ChengUCB/NequIP-LES.
The MatGL package with the LES implementation is available at https://github.com/ChengUCB/matgl.
The UMA package with the LES implementation is available at https://github.com/santi921/fairchem/tree/les_branch.
date_created: 2026-01-04T23:01:33Z
date_published: 2025-12-10T00:00:00Z
date_updated: 2026-08-07T09:35:25Z
day: '10'
department:
- _id: GradSch
- _id: BiCh
doi: 10.1021/acs.jctc.5c01400
external_id:
arxiv:
- '2507.14302'
pmid:
- '41368735 '
fulldoi: https://doi.org/10.1021/acs.jctc.5c01400
intvolume: ' 21'
issue: '24'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2507.14302
month: '12'
oa: 1
oa_version: Preprint
page: 12709-12724
pmid: 1
publication: Journal of Chemical Theory and Computation
publication_identifier:
eissn:
- 1549-9626
issn:
- 1549-9618
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: A universal augmentation framework for long-range electrostatics in machine
learning interatomic potentials
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 21
year: '2025'
...
---
_id: '17278'
abstract:
- lang: eng
text: An azeotrope is a constant boiling point mixture, and its behavior is important
for fluid separation processes. Predicting azeotropes from atomistic simulations
is difficult due to the complexities and convergence problems of Monte Carlo and
free-energy perturbation techniques. Here, we present a methodology for predicting
the azeotropes of binary mixtures, which computes the compositional dependence
of chemical potentials from molecular dynamics simulations using the S0 method
and employs experimental boiling point and vaporization enthalpy data. Using this
methodology, we reproduce the azeotropes, or lack thereof, in five case studies,
including ethanol/water, ethanol/isooctane, methanol/water, hydrazine/water, and
acetone/chloroform mixtures. We find that it is crucial to use the experimental
boiling point and vaporization enthalpy for reliable azeotrope predictions, as
empirical force fields are not accurate enough for these quantities. Finally,
we use regular solution models to rationalize the azeotropes and reveal that they
tend to form when the mixture components have similar boiling points and strong
interactions.
acknowledgement: B.C. thanks Alessandro Laio, who introduced the phenomenon of azeotrope
and suggested using the S0 method to compute it. B.C. and X.W. thank Felix Wodaczek
for the insightful comments and suggestions on the manuscript. B.C. and X.W. acknowledge
the resources provided by the Cambridge Tier-2 system operated by the University
of Cambridge Research Computing Service, funded by EPSRC Tier-2 capital (Grant No.
EP/P020259/1).
article_number: '034111'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Xiaoyu
full_name: Wang, Xiaoyu
id: 8dff9c62-32b0-11ee-9fa8-fc73025e10f3
last_name: Wang
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Wang X, Cheng B. Integrating molecular dynamics simulations and experimental
data for azeotrope predictions in binary mixtures. Journal of Chemical Physics.
2024;161(3). doi:10.1063/5.0217232
apa: Wang, X., & Cheng, B. (2024). Integrating molecular dynamics simulations
and experimental data for azeotrope predictions in binary mixtures. Journal
of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0217232
chicago: Wang, Xiaoyu, and Bingqing Cheng. “Integrating Molecular Dynamics Simulations
and Experimental Data for Azeotrope Predictions in Binary Mixtures.” Journal
of Chemical Physics. AIP Publishing, 2024. https://doi.org/10.1063/5.0217232.
ieee: X. Wang and B. Cheng, “Integrating molecular dynamics simulations and experimental
data for azeotrope predictions in binary mixtures,” Journal of Chemical Physics,
vol. 161, no. 3. AIP Publishing, 2024.
ista: Wang X, Cheng B. 2024. Integrating molecular dynamics simulations and experimental
data for azeotrope predictions in binary mixtures. Journal of Chemical Physics.
161(3), 034111.
mla: Wang, Xiaoyu, and Bingqing Cheng. “Integrating Molecular Dynamics Simulations
and Experimental Data for Azeotrope Predictions in Binary Mixtures.” Journal
of Chemical Physics, vol. 161, no. 3, 034111, AIP Publishing, 2024, doi:10.1063/5.0217232.
short: X. Wang, B. Cheng, Journal of Chemical Physics 161 (2024).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: All simulation setups, analysis scripts, and raw data in
the study are available in the SI repository https://github.com/Xiaoyu-Wang-Stone/Azeotrope_S0.
date_created: 2024-07-21T22:01:00Z
date_published: 2024-07-14T00:00:00Z
date_updated: 2026-08-07T10:38:59Z
day: '14'
department:
- _id: BiCh
- _id: GradSch
doi: 10.1063/5.0217232
external_id:
arxiv:
- '2405.02216'
isi:
- '001281819100016'
pmid:
- '39007379'
fulldoi: https://doi.org/10.1063/5.0217232
intvolume: ' 161'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2405.02216
month: '07'
oa: 1
oa_version: Preprint
pmid: 1
publication: Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
issn:
- 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/Xiaoyu-Wang-Stone/Azeotrope_S0
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Integrating molecular dynamics simulations and experimental data for azeotrope
predictions in binary mixtures
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 161
year: '2024'
...