New model predicts a chemical reaction’s point of no return and transition states

Good news! This could be a breakthrough in speed and simplicity!

"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. ... 

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. ...

These transition states are so fleeting that they’re nearly impossible to observe experimentally. ...

To create React-OT, the researchers trained it on the same dataset that they used to train their older model. These data contain structures of reactants, products, and transition states, calculated using quantum chemistry methods, for 9,000 different chemical reactions, mostly involving small organic or inorganic molecules. ..."

From the abstract:

"Transition states (TSs) are transient structures that are key to understanding reaction mechanisms and designing catalysts but challenging to capture in experiments.

Many optimization algorithms have been developed to search for TSs computationally. Yet, the cost of these algorithms driven by quantum chemistry methods (usually density functional theory) is still high, posing challenges for their applications in building large reaction networks for reaction exploration.

Here we developed React-OT, an optimal transport approach for generating unique TS structures from reactants and products.

React-OT generates highly accurate TS structures with a median structural root mean square deviation of 0.053 Å and median barrier height error of 1.06 kcal mol−1 requiring only 0.4 s per reaction. The root mean square deviation and barrier height error are further improved by roughly 25% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB.

We envision that the remarkable accuracy and rapid inference of React-OT will be highly useful when integrated with the current high-throughput TS search workflow. This integration will facilitate the exploration of chemical reactions with unknown mechanisms."

New model predicts a chemical reaction’s point of no return | MIT News | Massachusetts Institute of Technology "Chemists could use this quick computational method to design more efficient reactions that yield useful compounds, from fuels to pharmaceuticals."

Optimal transport for generating transition states in chemical reactions (open access)

Fig. 1: Overview of the diffusion model and optimal transport framework for generating TSs. [transition states]