Chemists at MIT have developed a machine learning model that can predict transition states in chemical reactions. Traditional quantum methods take hours or days to calculate a single state, but this model only takes a few seconds. It can handle small and large molecules, and may eventually incorporate catalysts for even faster predictions of reactions.
MIT Chemists Created a Machine Learning Model for Predicting Chemical Reactions
In chemistry, the transition state during a chemical reaction is crucial but difficult to observe. Traditional methods take hours or days to calculate just one transition state, hindering the design of new reactions or understanding nature’s changes.
Machine Learning Solution
MIT’s team developed a machine learning model trained on quantum chemistry data for 9,000 reactions. This model can understand different orientations of reactants, providing faster and more flexible solutions.
Practical Value
The model was tested on 1,000 new reactions, providing accurate solutions in just a few seconds, compared to the slow quantum method. It also surprisingly worked well for both small and large molecules, with future plans to incorporate catalysts for making new medicines or fuels.
Impact
This new method serves as a tool for chemists, predicting reaction changes faster and assisting in the discovery of new reactions.
For more information, check out the Paper and MIT Blog.
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- MIT Chemists Created a Machine Learning Model that can Predict the Structures Formed when a Chemical Reaction Reaches its Point of no Return
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