Reactivity Ratio

Reactivity ratio, a measure of the relative reactivity of monomers in polymerization or the propensity of substrates in chemical reactions, is a key parameter for predicting reaction outcomes and optimizing processes. Current research focuses on leveraging machine learning, particularly graph neural networks and transformer-based models, to predict reactivity ratios from molecular structures or textual descriptions of reaction procedures, improving accuracy and efficiency compared to traditional methods. These advancements are impacting diverse fields, from optimizing industrial robotic processes by predicting human-robot interaction dynamics to enabling more accurate predictions of chemical reaction yields and the design of novel chemical reactions. The ability to accurately predict reactivity ratios is crucial for accelerating materials discovery and improving the efficiency of chemical synthesis.