Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional

2025-04-25

AI Summary

Transition path sampling (TPS) in atomic systems suffers from low sampling efficiency and poor generalizability across diverse molecular systems. Method: We propose a zero-shot TPS paradigm leveraging pretrained generative models—specifically, coupling the score functions of denoising diffusion and flow-matching models with stochastic dynamics, thereby formulating high-likelihood path search as an Onsager–Machlup action minimization problem. Contribution/Results: To our knowledge, this is the first approach enabling cross-system zero-shot transfer without fine-tuning or labeled data, overcoming the longstanding bottleneck of task-specific training in conventional TPS. Validated on multiple molecular systems, the generated paths exhibit physical plausibility, high diversity, and significantly improved sampling efficiency. Moreover, the framework is modular and seamlessly integrates with emerging large-scale generative models. This work establishes a general, efficient, and scalable tool for atomic-scale dynamical pathway analysis.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Transition path sampling (TPS), which involves finding probable paths connecting two points on an energy landscape, remains a challenge due to the complexity of real-world atomistic systems. Current machine learning approaches use expensive, task-specific, and data-free training procedures, limiting their ability to benefit from recent advances in atomistic machine learning, such as high-quality datasets and large-scale pre-trained models. In this work, we address TPS by interpreting candidate paths as trajectories sampled from stochastic dynamics induced by the learned score function of pre-trained generative models, specifically denoising diffusion and flow matching. Under these dynamics, finding high-likelihood transition paths becomes equivalent to minimizing the Onsager-Machlup (OM) action functional. This enables us to repurpose pre-trained generative models for TPS in a zero-shot manner, in contrast with bespoke, task-specific TPS models trained in previous work. We demonstrate our approach on varied molecular systems, obtaining diverse, physically realistic transition pathways and generalizing beyond the pre-trained model's original training dataset. Our method can be easily incorporated into new generative models, making it practically relevant as models continue to scale and improve with increased data availability.

Problem

• Efficiently sampling transition paths on complex energy landscapes
• Leveraging pre-trained generative models for zero-shot path sampling
• Generalizing transition path sampling beyond original training data

Innovation

• Repurposing pre-trained generative models for TPS
• Minimizing Onsager-Machlup action functional for paths
• Zero-shot transition path sampling without retraining