Search papers, labs, and topics across Lattice.
This paper introduces optimized Fortran and Python software for efficiently calculating quartic force fields (QFFs) and performing vibrational second-order perturbation (VPT2) calculations using machine-learned potentials (MLPs). The software drastically reduces the computational cost of determining QFFs, enabling VPT2 calculations on large molecules like 21-atom aspirin in approximately one minute using a previously reported MLP. This allows for the study of quantum anharmonic effects in large molecules, which are typically approximated by classical molecular dynamics simulations.
Quantum anharmonic vibrational energies of a 21-atom molecule can now be computed on a laptop in minutes, thanks to a new software package leveraging machine-learned potentials.
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.
