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Assessing generative modeling approaches for free energy estimates in condensed matter
Maximilian Schebek1, Jiajun He2, Emil Hoffmann1
1Department of Physics, Freie Universität Berlin, 14195 Berlin, Germany.
The Journal of Chemical Physics
|May 12, 2026
Summary
Generative models accurately estimate free energy differences in molecular simulations, offering a computationally efficient alternative to traditional methods. Continuous flows and FEAT excel in energy evaluations, while discrete flows reduce inference costs.
Area of Science:
- Computational Chemistry
- Statistical Mechanics
- Materials Science
Background:
- Estimating free energy differences is crucial for molecular simulations but computationally demanding.
- Traditional methods require sampling many intermediate states, limiting efficiency and scalability.
Purpose of the Study:
- To benchmark generative models for free energy estimation in condensed-matter systems.
- To compare the efficiency, accuracy, and scalability of different generative approaches.
Main Methods:
- Review and benchmark discrete and continuous normalizing flows.
- Utilize FEAT (Free Energy Estimators with Adaptive Transport) with Jarzynski equality.
- Employ coarse-grained monatomic ice and Lennard-Jones solids as benchmark systems.
Main Results:
- All generative models achieved highly accurate free energy estimates.
- Generative methods, in some cases, required fewer energy evaluations than traditional approaches.
- Continuous flows and FEAT demonstrated high efficiency in energy evaluations; discrete flows offered lower inference costs.
Conclusions:
- Generative models provide accurate and potentially more efficient free energy calculations for condensed-phase systems.
- The choice between discrete and continuous flows depends on system-specific trade-offs between evaluation and inference costs.
- Open data facilitates future benchmarking of free energy estimation techniques.
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