In this work, we seek to simulate rare transitions between metastable states using score-based generative models. An efficient method for generating high-quality transition paths is valuable for the study of molecular systems since data is often difficult to obtain. We develop two novel methods for path generation in this paper: a chain-based approach and a midpoint-based approach. The first biases the original dynamics to facilitate transitions, while the second mirrors splitting techniques and breaks down the original transition into smaller transitions. Numerical results of generated transition paths for the M\"uller potential and for Alanine dipeptide demonstrate the effectiveness of these approaches in both the data-rich and data-scarce regimes.

翻译：本文旨在利用基于分数的生成模型模拟亚稳态之间的罕见跃迁。由于分子系统的数据通常难以获取，一种能够生成高质量过渡路径的高效方法对分子研究具有重要意义。我们提出了两种创新的路径生成方法：基于链的方法和基于中点的方法。前者通过偏置原始动力学促进跃迁，后者则借鉴分裂技术，将原始跃迁分解为多个较小的跃迁。针对米勒势和丙氨酸二肽的过渡路径生成数值结果，验证了这些方法在数据充足和数据稀缺两种场景下的有效性。