School of Engineering

Scientists from the Massachusetts Institute of Technology have used machine learning to expedite calculations of transition states in chemical reactions, a process that could support the invention of new reactions and catalysts with applications in fuels, pharmaceuticals and understanding the origins of life. Using a method known as density functional theory to compute transition states…

Scientists at MIT have developed a machine learning-based technique for rapidly calculating the transition state of a chemical reaction, a step that was previously extremely time-consuming using traditional quantum chemistry methods. The transition state is a crucial yet fleeting phase in any reaction, marking the point where molecules have gained enough energy for a reaction…

A team of MIT scientists has developed a machine learning-based model to calculate transition states during chemical reactions, a process which normally requires quantum computing and can take hours or even days to complete. Transition states, which inevitably occur during reactions when molecules reach a particular energy threshold, were previously calculated through quantum chemistry’s density…

MIT researchers have developed a machine learning-based technique that can rapidly calculate the structures of fleeting transition states during chemical reactions. Identifying and understanding these quasi-instantaneous moments, when molecules have collected enough energy to proceed with reaction, is crucial to fields such as catalyst design and natural system research. With traditional quantum chemistry-based techniques, it…