A team of MIT researchers has developed a machine learning model that can calculate transition states in chemical reactions within a few seconds, providing a faster alternative to traditional quantum chemistry techniques. The model could be used to design new reactions and catalysts, as well as model naturally occurring chemical reactions.
During a chemical reaction, molecules gain energy until they reach what's known as the transition state -- a point of no return from which the reaction must proceed. This state is so fleeting that it's nearly impossible to observe it experimentally. The structures of these transition states can be calculated using techniques based on quantum chemistry , but that process is extremely time-consuming.
A team of MIT researchers has now developed an alternative approach, based on machine learning, that can calculate these structures much more quickly -- within a few seconds. Their new model could be used to help chemists design new reactions and catalysts to generate useful products like fuels or drugs, or to model naturally occurring chemical reactions such as those that might have helped to drive the evolution of life on Eart
MIT Researchers Machine Learning Chemical Reactions Transition States Quantum Chemistry Catalysts Fuels Drugs Evolution Of Life
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