When chemists design new chemical reactions, one useful piece of information involves the reaction’s transition state — the point of no return from which a reaction must proceed.
This information allows chemists to try to produce the right conditions that will allow the desired reaction to occur. However, current methods for predicting the transition state and the path that a chemical reaction will take are complicated and require a huge amount of computational power.
MIT researchers have now developed a machine-learning model that can make these predictions in less than a second, with high accuracy. Their model could make it easier for chemists to design chemical reactions that could generate a variety of useful compounds, such as pharmaceuticals or fuels.
“We’d like to be able to ultimately design processes to take abundant natural resources and turn them into molecules that we need, such as materials and therapeutic drugs.