Organic chemists harness AI to uncover how fast chemical reactions proceed
Chemists routinely optimize reactions to maximize the yield of their desired products, but understanding why those reactions work can require laborious experiments that track reactions over time. Researchers at the University of Tokyo have developed a method to extract hidden information about reaction speeds from yield data obtained during reaction optimization, using machine learning and rate…
A novel technique developed by chemists at the University of Tokyo merges machine learning with reaction optimization to uncover hidden kinetic information. This method, known as Concentration-Dependent Yield Analysis (CYAN), can analyze yield data from reaction optimization experiments to estimate the speed of different reaction steps without the need for separate kinetic experiments.
By augmenting yield data with machine learning, CYAN can calculate rate constants and provide insights into reaction mechanisms. The researchers demonstrated the effectiveness of CYAN by applying it to a nickel-mediated reaction used to create large ring-shaped carbon molecules. The technique revealed that nickel accelerated the formation of the desired molecular ring while slowing a competing reaction that would produce unwanted byproducts.
This unexpected "template effect" slowed a secondary reaction, concentrating the reaction on the desired outcome.
CYAN was created by combining machine learning with chemists' hypotheses about reaction mechanisms. The machine learning component fills in gaps in the experimental data, creating a more comprehensive picture of how product concentrations change. Chemists then apply rate equations to extract rate constants from this enhanced yield data.
Although CYAN does not replace traditional kinetic experiments, it offers a practical way to extract valuable kinetic information from optimization yield data, potentially improving the design of new synthetic methods and reanalyzing historical experimental datasets.
Written by urgent.news from Phys.org's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
