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://ucris.univie.ac.at/portal/en/publications/molecular-hessian-matrices-from-a-machine-learning-random-forest-regression-algorithm(0fb3d9c8-96ac-408f-a53f-24b4e8bfd533).html