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Neural Thermodynamic Integration: Free Energies from Energy-Based Diffusion Models.
Bálint Máté1,2,3, François Fleuret2, Tristan Bereau1
1Institute for Theoretical Physics, Heidelberg University, 69120 Heidelberg, Germany.
The Journal of Physical Chemistry Letters
|November 6, 2024
Summary
Neural TI uses a neural network to perform thermodynamic integration (TI) for free-energy calculations. This approach accurately computes excess chemical potential in Lennard-Jones fluids from a single simulation, reducing computational cost.
Area of Science:
- Computational chemistry
- Statistical mechanics
- Machine learning
Background:
- Thermodynamic integration (TI) is a rigorous method for free-energy calculations.
- TI is computationally expensive and requires sampling many intermediate ensembles.
- Current TI methods are limited in the degrees of freedom they can handle.
Purpose of the Study:
- To develop a more efficient method for thermodynamic integration.
- To reduce the computational cost of free-energy calculations.
- To enable TI for systems with many degrees of freedom.
Main Methods:
- Proposed Neural TI, a method using a trainable neural network for TI.
- Parametrized a time-dependent Hamiltonian interpolating between systems.
- Optimized the Hamiltonian's gradient using a score matching objective.
- Utilized an energy-based diffusion model to sample intermediate ensembles.
Main Results:
- Neural TI accurately calculates excess chemical potential for Lennard-Jones fluids.
- The method performs TI from a single reference calculation.
- Achieved accurate free-energy changes without simulating intermediate Hamiltonians.
Conclusions:
- Neural TI offers a computationally efficient alternative to traditional TI.
- The method demonstrates the potential of machine learning in free-energy calculations.
- Neural TI can overcome limitations of current TI methods in terms of computational cost and system complexity.
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