Reaction Network

Reaction networks model the complex interactions of chemical species, aiming to predict reaction outcomes and design efficient synthetic pathways. Current research focuses on developing advanced machine learning models, including neural networks, normalizing flows, and optimal transport methods, to improve the accuracy and efficiency of reaction network simulations and predictions, particularly for stochastic and high-dimensional systems. These advancements are crucial for accelerating drug discovery, materials science, and biomanufacturing by enabling more accurate modeling of complex chemical processes and facilitating the design of novel molecules and reaction conditions. The development of more efficient algorithms for generating transition states and compact kinetic models is also a significant area of focus.