Automatic Refinement of Force Fields Based on Phase Diagrams

Exact characterization of phase transitions requires sufficient configurational sampling, necessitating efficient and accurate potential energy surfaces. Molecular force fields with computational efficiency and physical interpretability are desirable but challenging to refine for complex interactions. To address this, we propose a force field refinement strategy with phase diagrams as top-down optimization targets based on automatic differentiation. Using gas-liquid coexistence as a paradigm, we employ an enhanced sampling technique and design a differentiable loss function to evaluate force fields' depiction of phase diagrams. The refined force fields produce gas-liquid phase diagrams matching well with targets for two modeling systems, moreover, phase-equilibrium chemical potentials are also automatically determined during the refinement process, which confirms our approach as an effective automated force field development framework for phase transition studies.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2026-04-16
DOI
https://doi.org/10.1021/acs.jctc.5c02032
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Automatic Refinement of Force Fields Based on Phase Diagrams

Shenzhen Xu, Bin Han, Yu Kuang
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
article

Automatic Refinement of Force Fields Based on Phase Diagrams

Shenzhen Xu, Bin Han, Yu Kuang
article en

Abstract

Exact characterization of phase transitions requires sufficient configurational sampling, necessitating efficient and accurate potential energy surfaces. Molecular force fields with computational efficiency and physical interpretability are desirable but challenging to refine for complex interactions. To address this, we propose a force field refinement strategy with phase diagrams as top-down optimization targets based on automatic differentiation. Using gas-liquid coexistence as a paradigm, we employ an enhanced sampling technique and design a differentiable loss function to evaluate force fields' depiction of phase diagrams. The refined force fields produce gas-liquid phase diagrams matching well with targets for two modeling systems, moreover, phase-equilibrium chemical potentials are also automatically determined during the refinement process, which confirms our approach as an effective automated force field development framework for phase transition studies.

Journal of Chemical Theory and ComputationVol. 22(8)
Peking University (CN), Shenzhen Bay Laboratory (CN), Tsinghua–Berkeley Shenzhen Institute (CN)
National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China
Openalex Percentile: Top 100%
Machine Learning in Materials Science
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