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Potential energy or potential function plays an essential role in determining the stability of a mechanical system. If a system is subjected to both gravitational and elastic forces, the potential function of the system can be expressed as the algebraic sum of gravitational and elastic potential energy. If the system is in equilibrium and is displaced by a small amount, then the work done on the system equals the negative of the change in the system's potential energy from the initial to the...
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Nonadiabatic Force Matching for Alchemical Free-Energy Estimation.

Jorge L Rosa-Raíces1, David T Limmer1,2,3,4

  • 1Department of Chemistry, University of California, Berkeley, California 94720, United States.

Journal of Chemical Theory and Computation
|November 17, 2025
PubMed
Summary
This summary is machine-generated.

We introduce nonadiabatic force matching, a novel method using flow-based generative models to compute free-energy differences from nonadiabatic alchemical transformations. This approach significantly reduces simulation costs for free-energy calculations with minimal accuracy loss.

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Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Machine Learning

Background:

  • Calculating free-energy differences is crucial for molecular modeling.
  • Traditional methods like thermodynamic integration can be computationally expensive.
  • Nonadiabatic processes introduce complexities in free-energy calculations.

Purpose of the Study:

  • To develop a more efficient method for computing free-energy differences from nonadiabatic alchemical transformations.
  • To leverage flow-based generative models for enhanced accuracy and reduced computational cost.
  • To establish bounds on alchemical free energy using learned nonadiabatic force fields.

Main Methods:

  • Proposed a method called nonadiabatic force matching.
  • Utilized flow-based generative models and stochastic flow matching.
  • Learned a nonadiabatic force field to estimate dissipation during alchemical transformations.
  • Employed short-time trajectory data to evaluate variational bounds on free energy.

Main Results:

  • The nonadiabatic force matching method can substantially reduce simulation costs.
  • Achieved negligible loss of accuracy compared to traditional thermodynamic integration.
  • Demonstrated the ability to obtain upper and lower bounds on alchemical free energy.
  • Showcased the method's effectiveness in atomistic model free-energy calculations.

Conclusions:

  • Nonadiabatic force matching offers a computationally efficient alternative for free-energy calculations.
  • The method provides accurate estimates with reduced simulation requirements.
  • This approach advances the application of machine learning in computational chemistry.