Efficient Monte Carlo sampling of metastable systems using non-local collective variable updates
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911599758934016 |
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| author | Schönle, Christoph Carbone, Davide Gabrié, Marylou Lelièvre, Tony Stoltz, Gabriel |
| author_facet | Schönle, Christoph Carbone, Davide Gabrié, Marylou Lelièvre, Tony Stoltz, Gabriel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16812 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Monte Carlo sampling of metastable systems using non-local collective variable updates Schönle, Christoph Carbone, Davide Gabrié, Marylou Lelièvre, Tony Stoltz, Gabriel Statistical Mechanics Computational Physics 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. |
| title | Efficient Monte Carlo sampling of metastable systems using non-local collective variable updates |
| topic | Statistical Mechanics Computational Physics |
| url | https://arxiv.org/abs/2512.16812 |