用生成模型替代多温度副本，加速分子模拟的重构交换方法
Generative Replica-Exchange: A Flow-based Framework for Accelerating Replica Exchange Simulations
- 用归一化流生成高温构型，无需多温度训练数据
- 仅需一个目标温度副本，效率提升显著
- 适合复杂分子系统模拟，保持热力学严格性
重构交换（REX）是广泛使用的增强采样方法，但其效率受限于需要大量中间温度副本。本文提出生成式重构交换（GREX），将深度生成模型融入REX框架，消除温度阶梯需求。受储层重构交换（res-REX）启发，GREX利用训练好的归一化流按需生成高温构型，并以势能为约束直接映射到目标分布，无需目标温度训练数据。该方法将生产模拟简化为单一目标温度副本，同时通过马尔可夫接受准则保持热力学严格性。在三个复杂度递增的基准系统上验证了GREX的优越效率与实际适用性。
原文摘要 · Abstract (English)
Replica exchange (REX) is one of the most widely used enhanced sampling methodologies, yet its efficiency is limited by the requirement for a large number of intermediate temperature replicas. Here we present Generative Replica Exchange (GREX), which integrates deep generative models into the REX framework to eliminate this temperature ladder. Drawing inspiration from reservoir replica exchange (res-REX), GREX utilizes trained normalizing flows to generate high-temperature configurations on demand and map them directly to the target distribution using the potential energy as a constraint, without requiring target-temperature training data. This approach reduces production simulations to a single replica at the target temperature while maintaining thermodynamic rigor through Metropolis exchange acceptance. We validate GREX on three benchmark systems of increasing complexity, highlighting its superior efficiency and practical applicability for molecular simulations.
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