Fleeting transitions
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 Earth.
Modeling reactions
Although the researchers trained their model primarily on reactions involving compounds with a relatively small number of atoms — up to 23 atoms for the entire system — they found that it could also make accurate predictions for reactions involving larger molecules.
“Even if you look at bigger systems or systems catalyzed by enzymes, you’re getting pretty good coverage of the different types of ways that atoms are most likely to rearrange,” Kulik says.
The researchers now plan to expand their model to incorporate other components such as catalysts, which could help them investigate how much a particular catalyst would speed up a reaction. This could be useful for developing new processes for generating pharmaceuticals, fuels, or other useful compounds, especially when the synthesis involves many chemical steps.
“Traditionally all of these calculations are performed with quantum chemistry, and now we’re able to replace the quantum chemistry part with this fast generative model,” Duan says.
Another potential application for this kind of model is exploring the interactions that might occur between gases found on other planets, or to model the simple reactions that may have occurred during the early evolution of life on Earth, the researchers say.
The new method represents “a significant step forward in predicting chemical reactivity,” says Jan Halborg Jensen, a professor of chemistry at the University of Copenhagen, who was not involved in the research.
“Finding the transition state of a reaction and the associated barrier is the key step in predicting chemical reactivity, but also the one of the hardest tasks to automate,” he says. “This problem is holding back many important fields such as computational catalyst and reaction discovery, and this is the first paper I have seen that could remove this bottleneck.”
The research was funded by the U.S. Office of Naval Research and the National Science Foundation.Kindly read our copyright disclaimer here: https://cere-sync.com/dmca-copyrights-disclaimer/