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Several methods are available to compute the anharmonicity in semi-rigidmolecules. However, such methods are not routinely employed yet because oftheir large computational cost, especially for large molecules. The potentialenergy surface is required and generally approximated by a quartic force fieldpotential based on ab initio calculation, thus limiting this approach tomedium-sized molecules. We developed a new, fast and accurate hybrid QuantumMechanic/Machine learning (QM//ML) approach to reduce the computational timefor large systems. With this novel approach, we evaluated anharmonicfrequencies of 37 molecules thus covering a broad range of vibrational modesand chemical environments. The obtained fundamental frequencies reproduceresults obtained using B2PLYP/def2tzvpp with a root-mean-square deviation(RMSD) of 21 cm-1 and experimental results with a RMSD of 23 cm-1. Along withthis very good accuracy, the computational time with our hybrid QM//ML approachscales linearly with N while the traditional full ab initio method scales asN2, where N is the number of atoms.

Combining quantum mechanics and machine-learning calculations for anharmonic corrections to vibrational frequencies