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arXiv · 2512.16812

Efficient Monte Carlo sampling of metastable systems using non-local collective variable updates

Christoph Schönle·Davide Carbone·Marylou Gabrié·Tony Lelièvre·Gabriel Stoltz
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Abstract

Monte Carlo simulations are widely used to simulate complex molecular systems, but standard approaches suffer from metastability. Lately, the use of non-local proposal updates in a collective-variable (CV) space has been proposed in several works. Here, we generalize these approaches and explicitly spell out an algorithm for non-linear CVs and underdamped Langevin dynamics. We prove reversibility of the resulting scheme and demonstrate its performance on several numerical examples, observing a substantial performance increase compared to methods based on overdamped Langevin dynamics as considered previously. Advances in generative machine-learning-based proposal samplers now enable efficient sampling in CV spaces of intermediate dimensionality (tens to hundreds of variables), and our results extend their applicability toward more realistic molecular systems.

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BibTeXRIS

Christoph Schönle, Davide Carbone, Marylou Gabrié, Tony Lelièvre, Gabriel Stoltz. 2025-12-18. Efficient Monte Carlo sampling of metastable systems using non-local collective variable updates. https://doi.org/10.1063/5.0318016

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