Transition Path

Transition path analysis focuses on identifying the most probable pathways between metastable states in complex systems, a crucial task in fields like chemistry and biology where rare events govern system behavior. Current research emphasizes developing efficient computational methods, employing machine learning techniques such as neural networks (including variational autoencoders and deep reinforcement learning algorithms like DDPG), and normalizing flows to sample and predict these paths, often without relying on pre-defined collective variables. These advancements improve the accuracy and efficiency of simulations, enabling deeper understanding of diverse phenomena ranging from molecular conformational changes to socio-ecological transitions, and facilitating applications in material design and drug discovery.