Computational spectroscopy using MULTIMODE and machine-learned potentials

Abstract

Computational vibrational spectroscopy beyond the harmonic approximation relies on the molecular potential and ideally dipole and possibly higher moments of charge distributions. In the past decade, there has been a paradigm shift in generating highly accurate Machine-Learned potentials (MLPs). These are precise fits to thousands of electronic energies, using modern methods of regression. With such MLPs, it is possible to combine these with a variety of post-harmonic quantum methods ranging from perturbation theory to full variational calculations. After a short review of these methods, we focus on vibrational self-consistent field and configuration interaction (VSCF + VCI) calculations, as implemented in the code MULTIMODE. Two applications of this software to complex parts of the infrared spectra of formic acid dimer and the protonated oxalate anion are presented. Two new interfaces to MULTIMODE are then given. One is a Python-based GUI to enable user-friendly input to MULTIMODE. The second interface, PyFort, which is written in Fortran, uses MLPs written in Python in MULTIMODE via a C wrapper. Demonstrations of this are given for a PhysNet potential of Meuwly and co-workers for protonated oxalate anion (C2O4H−) and for the “universal” force field MACE-OFF of Csányi and co-workers. MULTIMODE VSCF + VCI vibrational energies of C2O4H− using the PhysNet MLP agree well with those using a permutationally invariant potential, trained on the datasets used to train the PhysNet MLP. A test of the MACE-OFF interface is done for H2CO. The PyFort software for both these examples is provided in the supplementary material.