Molecular Hessian matrices from a machine learning random forest regression algorithm

Author(s)
Giorgio Domenichini, Christoph Dellago
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.

Organisation(s)
Computational Materials Physics, Computational and Soft Matter Physics
Journal
Journal of Chemical Physics
Volume
159
No. of pages
12
ISSN
0021-9606
DOI
https://doi.org/10.48550/arXiv.2307.16512
Publication date
11-2023
Peer reviewed
Yes
Austrian Fields of Science 2012
103006 Chemical physics, 103043 Computational physics
ASJC Scopus subject areas
Physics and Astronomy(all), Physical and Theoretical Chemistry
Portal url
https://ucrisportal.univie.ac.at/en/publications/molecular-hessian-matrices-from-a-machine-learning-random-forest-regression-algorithm(0fb3d9c8-96ac-408f-a53f-24b4e8bfd533).html