Molecular Hessian matrices from a machine learning random forest regression algorithm

doi:10.1063/5.0169384

Molecular Hessian matrices from a machine learning random forest regression algorithm

2023-11-20

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DOI：10.1063/5.0169384

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In this article, we present a machine learning model to obtain fast and accurate estimates of the molecular Hessian matrix. In this model, based on a random forest, the second derivatives of the energy with respect to redundant internal coordinates are learned individually. The internal coordinates together with their specific representation guarantee rotational and translational invariance. The model is trained on a subset of the QM7 dataset but is shown to be applicable to larger molecules picked from the QM9 dataset. From the predicted Hessian, it is also possible to obtain reasonable estimates of the vibrational frequencies, normal modes, and zero point energies of the molecules. (c) 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). https://doi.org/10.1063/5.0169384

Keywords:

GEOMETRY OPTIMIZATION

EQUILIBRIUM GEOMETRIES

FORCE-CONSTANTS

WAVE-FUNCTION

HARTREE-FOCK

ENERGY

SPECTROSCOPY

INTENSITIES

MECHANICS

CHEMISTRY

Molecular Hessian matrices from a machine learning random forest regression algorithm

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