Machine Learning in Chemical Kinetics: Predictions, Mechanistic Analysis, and Reaction Optimization.

Abstract

Chemical kinetics is a core area of physical chemistry that examines the speeds of chemical processes and the mechanisms that underlie them. Machine learning (ML) techniques have become effective tools for improving understanding and prediction in this area. The use of machine learning in chemical kinetics research is examined in this abstract, with particular emphasis on how it can be used to forecast A deeper knowledge of reaction kinetics is made possible by the capability of machine learning (ML) to handle high-dimensional data and learn from many chemical systems. This opens up significant opportunities for developing new chemical processes, improving catalysis, and hastening the discovery of new materials.