Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution

Abstract

We show how alchemical free energies can be calculated with QML potentials to identify deficiencies in RRHO approximations for computing tautomeric free energies, and how these potentials can be learned from experiment to improve prediction accuracy.