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Generative Replica Exchange (GREX) is introduced, a novel method that integrates normalizing flows into replica exchange simulations to eliminate the need for intermediate temperature replicas. GREX uses trained normalizing flows to generate high-temperature configurations and maps them to the target distribution using potential energy constraints, circumventing the need for target-temperature training data. Validated on three benchmark systems, GREX demonstrates superior efficiency and practical applicability by reducing production simulations to a single replica at the target temperature while maintaining thermodynamic rigor.
Ditch the temperature ladder: Generative Replica Exchange (GREX) uses normalizing flows to generate high-temperature configurations on-demand, slashing the computational cost of replica exchange simulations.
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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