Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents

TL;DR

Hessian QM9 is a comprehensive database of molecular Hessians in various solvents, enabling improved machine learning models for vibrational frequency prediction and realistic solvent environment simulations.

Contribution

This work introduces the first database of molecular Hessians in implicit solvents, enhancing MLIP training with second derivatives for better vibrational predictions.

Findings

Including Hessians improves vibrational frequency predictions.

The dataset covers molecules in vacuum and multiple solvents.

Enhanced ML models better simulate molecules in realistic environments.

Abstract

A significant challenge in computational chemistry is developing approximations that accelerate \emph{ab initio} methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface. We present Hessian QM9, the first database of equilibrium configurations and numerical Hessian matrices, consisting of 41,645 molecules from the QM9 dataset at the $\omega$B97x/6-31G* level. Molecular Hessians were calculated in vacuum, as well as water, tetrahydrofuran, and toluene using an implicit solvation model. To demonstrate the utility of this dataset, we show that incorporating second derivatives of the potential energy surface into the loss function of a MLIP significantly improves the prediction of vibrational frequencies in all solvent environments, thus making this dataset extremely useful for studying organic molecules in realistic solvent environments for experimental characterization.