Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates.

ABSTRACT: Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG^‡_solv, ΔΔH^‡_solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal mol^-1 for ΔΔG

SUBMITTER: Chung Y 

PROVIDER: S-EPMC10866337 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature