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Combining Quantum Mechanics and Machine-Learning Calculations for Anharmonic Corrections to Vibrational Frequencies
journal contribution
posted on 2020-02-21, 16:33 authored by Julien Lam, Saleh Abdul-Al, Abdul-Rahman AlloucheSeveral
methods are available to compute the anharmonicity in semirigid
molecules. However, such methods are not yet routinely employed because
of their high computational cost, especially for large molecules.
The potential energy surface is required and generally approximated by a quartic force field
potential based on ab initio calculation, thus limiting this approach
to medium-sized molecules. We developed a new, fast, and accurate
hybrid quantum mechanics/machine learning (QM/ML) approach to reduce
the computational time for large systems. With this novel approach,
we evaluated anharmonic frequencies of 37 molecules, thus covering
a broad range of vibrational modes and chemical environments. The
obtained fundamental frequencies reproduce results 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 with this very good accuracy, the computational
time with our hybrid QM/ML approach scales linearly with N, while the traditional full ab initio method scales as N2, where N is the number of atoms.
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Keywords
cmroot-mean-square deviationnovel approachAnharmonic Correctionsanharmonic frequenciesN 2quartic force fieldvibrational modesRMSDMachine-Learning Calculationssemirigid moleculesB 2PLYPchemical environmentsenergy surfaceVibrational Frequenciesab initio method scalesQuantum MechanicsfrequencyQM37 moleculesab initio calculation
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