用生成模型从经典模拟数据恢复量子统计，速度快且可跨温度迁移。
Quantum statistics from classical simulations via generative Gibbs sampling
- 用生成模型学习单粒子条件分布，结合吉布斯采样重构量子态。
- 在标准测试体系上比路径积分分子动力学快数倍，节省大量计算时间。
- 适合需要快速获取量子效应的分子模拟研究者，尤其关注效率与泛化能力。
精确模拟核量子效应对分子建模至关重要，但路径积分分子动力学（PIMD）计算成本高。本文提出基于环聚合物的GG-PI框架，通过生成模型学习单粒子条件密度，并结合吉布斯采样，从经典模拟数据中恢复量子统计特性。该方法利用廉价的标准经典模拟或已有数据进行训练，无需重新训练即可跨温度迁移。在标准测试系统上，GG-PI显著缩短了实际运行时间，相比传统PIMD有明显加速。本方法具有良好的扩展性，适用于具有类似马尔可夫结构的多种问题。
原文摘要 · Abstract (English)
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.
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