提出新型自由能估计算法，统一平衡与非平衡方法。
FEAT: Free energy Estimators with Adaptive Transport
- 用随机插值学习传输路径，实现高效自由能估计。
- 基于受控的克鲁克斯定理和乔丹斯基等式，误差更小。
- 适合分子模拟、量子场论等需要精确自由能计算的领域。
我们提出自由能估计算法（FEAT），一种用于自由能估计的新框架——这一问题在多个科学领域中至关重要。FEAT 利用通过随机插值实现的学习传输路径，基于受控的克鲁克斯定理和受护的乔丹斯基等式，提供一致且方差最小的估计器，并附带自由能差的变分上下界。该框架将平衡与非平衡方法统一于同一理论体系下，为神经网络驱动的自由能计算奠定了原则性基础。在模型示例、分子模拟和量子场论中的实验验证表明，其性能优于现有基于学习的方法。代码已开源：https://github.com/jiajunhe98/FEAT。
原文摘要 · Abstract (English)
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.
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