Molecular Hessian matrices from a machine learning random forest regression algorithm

Author:

Domenichini Giorgio1ORCID,Dellago Christoph1ORCID

Affiliation:

1. Faculty of Physics, University of Vienna , Kolingasse 14-16, 1090 Vienna, Austria

Abstract

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.

Funder

Austrian Science Fund

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

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