Accelerating Physics-based Models with Generative Neural Networks
Over the past few years, the potential of machine learning methods that can generate text and images has expanded significantly. This project seeks to explore the implications of these advanced techniques for simulating and characterizing chemical and biological systems computationally. While physics-based models of biomolecular systems have been studied extensively, the computational burden of conducting simulations can be prohibitively high, hindering progress on important questions, e.g. small molecule drug design.