mlsanityData to 14 Oct 2026

FEAT: Free energy Estimators with Adaptive Transport

arXiv 2504.11516 ↗First posted 15 Apr 2025stat.ML cs.LG physics.chem-phcited by 16

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Abstract (arXiv)

We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled Crooks theorem, alongside variational upper and lower bounds on free energy differences. Unifying equilibrium and non-equilibrium methods under a single theoretical framework, FEAT establishes a principled foundation for neural free energy calculations. Experimental validation on toy examples, molecular simulations, and quantum field theory demonstrates improvements over existing learning-based methods. Our PyTorch implementation is available at https://github.com/jiajunhe98/FEAT.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 29 of its 63 references that are in the map. First posted on arXiv 15 Apr 2025.

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29 of 63 references.

References · 29

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  3. 2022Flow Matching for Generative Modelingcited by 5,702
  4. 2018Neural Ordinary Differential Equationscited by 3,779
  5. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  6. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
  7. 2016Variational Inference: A Review for Statisticianscited by 1,472
  8. 2022Building Normalizing Flows with Stochastic Interpolantscited by 995
  9. 2021E(n) Equivariant Graph Neural Networkscited by 965
  10. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 939
  11. 2023Improving and generalizing flow-based generative models with minibatch optimal transportcited by 879
  12. 2015Importance Weighted Autoencoderscited by 456
17 more
  1. 2023Equivariant flow matchingcited by 135
  2. 2022Flow Annealed Importance Sampling Bootstrapcited by 127
  3. 2021Annealed Flow Transport Monte Carlocited by 90
  4. 2021SE(3) Equivariant Graph Neural Networks with Complete Local Framescited by 76
  5. 2024NETS: A Non-Equilibrium Transport Samplercited by 69
  6. 2020Targeted free energy estimation via learned mappingscited by 67
  7. 2024Particle Denoising Diffusion Samplercited by 65
  8. 2024Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Samplingcited by 56
  9. 2024Target Score Matchingcited by 50
  10. 2024Transferable Boltzmann Generatorscited by 46
  11. 2023Learning Interpolations between Boltzmann Densitiescited by 43
  12. 2024Liouville Flow Importance Samplercited by 25
  13. 2024Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Modelscited by 14
  14. 2022Towards Healing the Blindness of Score Matchingcited by 13
  15. 2025Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyondcited by 11
  16. 2019Stochastic Variational Inference via Upper Boundcited by 8
  17. 2024Solvation Free Energies from Neural Thermodynamic Integrationcited by 8

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Citing papers per year: 2025: 5; 2026: 11.

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  1. 2025Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inferencecited by 23
  2. 2025RNE: plug-and-play diffusion inference-time control and energy-based trainingcited by 17
  3. 2025CREPE: Controlling Diffusion with Replica Exchangecited by 11
  4. 2026Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matchingcited by 10
  5. 2026Discrete Adjoint Schr"odinger Bridge Samplercited by 4
  6. 2025Assessing generative modeling approaches for free energy estimates in condensed mattercited by 3
  7. 2025Estimating Solvation Free Energies with Boltzmann Generatorscited by 1
  8. 2026ATLAS: A Foundation Neural Sampler for Amorphous Materialscited by 1
  9. 2026Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEscited by 0
  10. 2026FES-FM: Free Energy Surface Sampling via Reduced Flow Matchingcited by 0
  11. 2026CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimationcited by 0
  12. 2026Unsupervised Thermodynamics of Molecular Diffusion Models: Action-Operator Semantics and Auditable Free-Energy Readoutcited by 0
4 more
  1. 2026AquaGen: Scaling generative models to molecular dynamics precision on thousands of atomscited by 0
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  3. 2026Fixed-point neural samplers on discrete spacescited by 0
  4. 2026ENCORE: Exact Non-equilibrium COntrol with Replica Exchange for Diffusion Generationcited by 0