VPT2 Calculations of Vibrational Energies of CH3COOC6H4COOH Done in Seconds on a Laptop Using a Machine Learned Potential

TL;DR

This paper introduces software for rapid VPT2 vibrational energy calculations using machine-learned potentials, enabling efficient anharmonic analysis of large molecules like aspirin on a laptop.

Contribution

It presents new Fortran and Python tools that significantly reduce computational time for QFF and VPT2 calculations with ML potentials on large molecules.

Findings

First quantum anharmonic vibrational energies for large molecules like aspirin.

Calculation of 32,509 cubic force constants in about one minute.

Demonstrates efficient anharmonic analysis on a standard laptop.

Abstract

The determination of quartic force fields for use in vibrational second-order perturbation (VPT2) calculations, currently available in numerous electronic structure packages, becomes very expensive as the size of the molecule increases, especially if high-level coupled cluster theory is used. Machine-learned potentials (MLPs) for large molecules and clusters offer a viable alternative to obtain the quartic force field (QFF). Here, we report Fortran and Python software to determine the QFF and perform VPT2 calculations of energies from MLPs. We describe this software briefly and then apply it to \ce{H2O} and protonated oxalate as test cases. The Fortran software is applied to 21-atom aspirin, using a fast MLP reported by us. Despite the fact that there are 32,509 unique cubic force constants for aspirin, the computer time to calculate them using this MLP is trivial, i.e., around one minute. These results are the first quantum anharmonic ones for such a large molecule. The present protocol offers an efficient way to study quantum anharmonic effects for vibrational energies in large molecules. Currently, these are obtained overwhelmingly from classical molecular dynamics simulations, which cannot describe strong anharmonicity.