This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: 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 equations developed by chemists.
The study is published in the journal Advanced Science. Concentration-dependent yield analysis (CYAN) uses machine learning to expand the yield data from reaction optimization experiments, then uses that information to calculate how quickly the different steps of a reaction occur. This gives chemists a tool to understand, improve and design complex, high-yield chemical reactions.
To make a desired molecule, chemists carry out experiments to find the best conditions, changing variables such as concentration, temperature or the times at which ingredients are added. These optimization experiments reveal which conditions achieve the highest yield and can sometimes provide clues about why a reaction works. However, understanding reaction mechanisms in detail has traditionally required separate kinetic experiments that follow how a reaction changes over time.
CYAN connects these approaches by extracting kinetic information from the yield data generated during reaction optimization. "For years, chemists have treated these as two different objectives. We optimize reactions to achieve the highest yield, and if we want to understand the mechanism, we perform another series of experiments that follows the reaction over time.
Our goal with CYAN was to bring those two activities together," said Hiroyuki Isobe, a professor in the Department of Chemistry. "With CYAN, machine learning first augments the yield data obtained from our experiments. Chemists then apply rate equations based on their hypotheses about the reaction mechanism to extract rate constants from these data.
In this way, a single set of experiments can be used for both reaction optimization and kinetic analysis." The researchers developed CYAN by combining machine learning with mathematical descriptions of how they think a reaction works. Machine learning fills in the gaps between the experimental results, creating a more complete picture of how the amount of product changes under different conditions. The researchers then use this information to estimate the speed of different steps in the reaction, without needing separate experiments that measure the reaction repeatedly over time.
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