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Fundamental Research

Machine learning accelerates access to high-precision data for chemistry via ab initio molecular dynamics

Ab initio calculations consist in solving the Schrödinger equation for a set of atoms representing a chemical system of interest. In chemical reactivity, ab initio molecular dynamics (AIMD) make it possible, for example, to predict rate constants with a high degree of accuracy, such as in the case of zeolites with protons as active sites. Nevertheless, the conditions for obtaining results with the required accuracy can involve excessive calculation times. A recently developed method (Machine Learning Perturbation Theory) makes it possible to overcome this obstacle. The chosen application is that of alkene isomerization and cracking in large-pore zeolites.