Skip to content Skip to footer

The computational model successfully records the hard-to-capture transition stages of chemical reactions.

A team of researchers at the Massachusetts Institute of Technology (MIT) has developed a machine learning-based method to swiftly calculate the structures of transition states, crucial moments in chemical reactions. This state, at which molecules attain the necessary energy for a reaction, is important but fleetingly transient and difficult to experimentally observe. Calculating these structures traditionally requires a time-consuming process of quantum chemistry. However, the MIT team’s alternative approach takes only seconds.

The findings from the MIT team promise to help in the design of new reactions and catalysts for producing essential materials such as fuels or drugs. It could also assist in understanding chemical reactions that naturally occur, for instance, those that facilitated the evolution of life on Earth. The machine learning model was trained using structures of reactants, products, and transition states from 9,000 chemical reactions, which were previously calculated using quantum computation methods. When tested on about 1,000 unseen reactions, the model demonstrated a high degree of accuracy, comparable with transition state structures created using quantum techniques. However, the new method took only seconds for each reaction rather than hours or days.

The researchers hope to further enhance the model by integrating other components like catalysts, which would help in understanding how much a reaction’s speed could be bolstered by a particular catalyst. This would facilitate the development of new procedures for creating pharmaceuticals, fuels, or other functional compounds. The speed and accuracy of the new generative model could revolutionize the field, enabling thousands of transition states to be generated in the time it currently takes to create only a few. Furthermore, the research team believes the model could be used to investigate chemical reactions that may have occurred early in Earth’s evolution or interactions occurring between gases on other planets.

In addition to fueling advancements in computational catalyst and reaction discovery, the scientific method is hailed as a crucial step forward in predicting chemical reactivity, particularly for its potential to expedite one of the sector’s most challenging tasks: automating the finding of a reaction’s transition state and associated barrier. The study, authored by Chenru Duan, Heather Kulik among others, is published in the journal Nature Computational Science.

Leave a comment

0.0/5