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.
@article{arxiv.2601.20228,
title = {Quantum statistics from classical simulations via generative Gibbs sampling},
author = {Weizhou Wang and Xuanxi Zhang and Jonathan Weare and Aaron R. Dinner},
journal= {arXiv preprint arXiv:2601.20228},
year = {2026}
}