Application of machine learning in the study of biomolecular/chemical rate processes

This project involves the study of chemical rate processes, transport and reactions with the help of machine learning approaches. The rates of these processes usually depends on the chemical environment. The rate constant can vary over several orders of magnitude. Machine learning techniques will be used to predict the rate constant as a function of the chemical environment. In recent times, neural networks have been successfully employed in this context. However, the applications so far are limited to fairly simple systems. The goal will be to extend the approach to study of biomolecular systems, aqueous phases, and chemical reactions on surfaces.