Tracking large chemical reaction networks and rare events by neural networks
NNCME-2 is an advanced neural network framework that efficiently models high-dimensional chemical reaction networks using Variational Autoregressive Networks (VAN). It improves computational speed and accurately characterizes rare events, which are difficult to study in these complex systems. The method uses sophisticated optimization techniques and enhanced sampling strategies to resolve low-probability regions, making it a scalable tool for studying stochastic dynamics.