Chemical potentials from structure factors: I. Neutral multi-component mixtures
Abstract
The chemical potentials of multi-component mixtures underlie many physical and chemical phenomena, but remain challenging to compute. The S0 method enables the computation of chemical potentials from equilibrium molecular dynamics simulations, by leveraging the thermodynamic relationship between particle number fluctuations and derivatives of chemical potentials, followed by numerical integration along different compositions. Here we generalize the S0 method from two-component mixtures to neutral multi-component mixtures. We first extend the statistical mechanical formalism to high-dimensional compositional space, and then introduce a Gaussian process integration scheme combined with active learning to efficiently integrate chemical potentials and sample diverse compositions. We use this method to compute the mixing free energies of a molten metal alloy, and the solubilities of two paracetamol polymorphs in water-ethanol solvents. The extended S0 method provides a practical and scalable route for computing chemical potentials in neutral bulk multi-component mixtures from atomistic simulations.
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BibTeX
@misc{savoj2026chemical,
title = {{Chemical potentials from structure factors: I. Neutral multi-component mixtures}},
author = {Roya Savoj and Xiaoyu Wang and Musahid Ahmed and Bingqing Cheng},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2608.08357},
eprint = {2608.08357},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.08357},
}
RIS
TY - UNPB
TI - Chemical potentials from structure factors: I. Neutral multi-component mixtures
AU - Roya Savoj
AU - Xiaoyu Wang
AU - Musahid Ahmed
AU - Bingqing Cheng
PY - 2026
DA - 2026/11/02
PB - arXiv
DO - 10.48550/arxiv.2608.08357
UR - https://arxiv.org/abs/2608.08357
AB - The chemical potentials of multi-component mixtures underlie many physical and chemical phenomena, but remain challenging to compute. The S0 method enables the computation of chemical potentials from equilibrium molecular dynamics simulations, by leveraging the thermodynamic relationship between particle number fluctuations and derivatives of chemical potentials, followed by numerical integration along different compositions. Here we generalize the S0 method from two-component mixtures to neutral multi-component mixtures. We first extend the statistical mechanical formalism to high-dimensional compositional space, and then introduce a Gaussian process integration scheme combined with active learning to efficiently integrate chemical potentials and sample diverse compositions. We use this method to compute the mixing free energies of a molten metal alloy, and the solubilities of two paracetamol polymorphs in water-ethanol solvents. The extended S0 method provides a practical and scalable route for computing chemical potentials in neutral bulk multi-component mixtures from atomistic simulations.
ER -