Intramolecular proton transfer reaction dynamics using machine-learned ab initio potential energy surfaces

Hydrogen bonding interactions, which are central to various physicochemical processes, are investigated in the present study using ab initio-based machine learning potential energy surfaces. Abnormally strong intramolecular O-HMIDLINE HORIZONTAL ELLIPSISO hydrogen bonds, occurring in & beta;-diketone enols of malonaldehyde and its derivatives, with substituents ranging from various electron-withdrawing to electron-donating functional groups, are studied. Machine learning force fields were constructed using a kernel-based force learning model employing ab initio molecular dynamics reference data. These models were used for molecular dynamics simulations at finite temperature, and dynamical properties were determined by computing proton transfer free-energy surfaces. The chemical systems studied here show progression toward barrier-less proton transfer events at an accuracy of correlated electronic structure methods. Markov state models of the conformational states indicate shorter intramolecular hydrogen bonds exhibiting higher proton transfer rates. We demonstrate how functional group substitution can modulate the strength of intramolecular hydrogen bonds by studying the thermodynamic and kinetic properties.