Automatic Refinement of Force Fields Based on Phase Diagrams
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.
Fields of Study & Subjects
Machine Learning in Materials Science
Crystallography and molecular interactions
Block Copolymer Self-Assembly
Publication details
- Journal / source
- Journal of Chemical Theory and Computation
- Volume
- 22
- Issue
- 8
- Pages
- 3872–3880
- Publication date
- 16 April 2026
- Language
- EN