Quantum statistics from classical simulations via generative Gibbs sampling
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914285891878912 |
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| author | Wang, Weizhou Zhang, Xuanxi Weare, Jonathan Dinner, Aaron R. |
| author_facet | Wang, Weizhou Zhang, Xuanxi Weare, Jonathan Dinner, Aaron R. |
| contents | Accurate simulation of nuclear quantum effects is essential for molecular modeling but expensive using path integral molecular dynamics (PIMD). We present GG-PI, a ring-polymer-based framework that combines generative modeling of the single-bead conditional density with Gibbs sampling to recover quantum statistics from classical simulation data. GG-PI uses inexpensive standard classical simulations or existing data for training and allows transfer across temperatures without retraining. On standard test systems, GG-PI significantly reduces wall clock time compared to PIMD. Our approach extends easily to a wide range of problems with similar Markov structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20228 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum statistics from classical simulations via generative Gibbs sampling Wang, Weizhou Zhang, Xuanxi Weare, Jonathan Dinner, Aaron R. Chemical Physics Statistical Mechanics Machine Learning Computational Physics Accurate simulation of nuclear quantum effects is essential for molecular modeling but expensive using path integral molecular dynamics (PIMD). We present GG-PI, a ring-polymer-based framework that combines generative modeling of the single-bead conditional density with Gibbs sampling to recover quantum statistics from classical simulation data. GG-PI uses inexpensive standard classical simulations or existing data for training and allows transfer across temperatures without retraining. On standard test systems, GG-PI significantly reduces wall clock time compared to PIMD. Our approach extends easily to a wide range of problems with similar Markov structure. |
| title | Quantum statistics from classical simulations via generative Gibbs sampling |
| topic | Chemical Physics Statistical Mechanics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2601.20228 |