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 to proceed, and its likelihood of formation is a key factor in determining the likelihood of a reaction occurring. The team modelled transition states using a new computational technique based on a diffusion model, a type of machine learning that learns the processes most likely to yield a particular end result. This enabled the researchers to depict the interaction of two reactant molecules from any angle, instead of treating them as separate entities. Using data from 9,000 different chemical reactions, they trained the model to predict the transition state of future reactions, achieving a high degree of accuracy within a few seconds. The breakthrough could help in designing new reactions and catalysts, shed light on how natural systems function chemically, and potentially model the chemical processes involved in the evolution of life.
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