arXiv Machine Learning September 18, 2026 By Maximilian Negedly, Sebastian Falkner, Alessandro Coretti, Christoph Dellago

Correlation-Free Transition Path Sampling through Shooting Point Generation Guided by Committor Learning

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The paper introduces GenAIMMD, an iterative algorithm that learns the committor function and trains a conditioned Boltzmann Generator to generate uncorrelated transition paths without prior knowledge of the reaction coordinate. This method combines transition path sampling with committor learning, enabling fully parallelizable sampling. Benchmarks on a toy model and a polymer system show a substantial performance improvement over standard TPS.

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