Molecular Hessian matrices from a machine learning random forest regression algorithm

Autor(en)

Giorgio Domenichini, Christoph Dellago

Abstrakt

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(en)

Computergestützte Materialphysik, Computergestützte Physik und Physik der Weichen Materie

Journal

Journal of Chemical Physics

Band

159

Anzahl der Seiten

12

ISSN

0021-9606

DOI

https://doi.org/10.48550/arXiv.2307.16512

Publikationsdatum

11-2023

Peer-reviewed

Ja

ÖFOS 2012

103006 Chemische Physik, 103043 Computational Physics

ASJC Scopus Sachgebiete

Allgemeine Physik und Astronomie, Physical and Theoretical Chemistry

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/0fb3d9c8-96ac-408f-a53f-24b4e8bfd533