Derivative learning of tensorial quantities—Predicting finite temperature infrared spectra from first principles

Author(s)
Bernhard Schmiedmayer, Georg Kresse
Abstract

We develop a strategy that integrates machine learning and first-principles calculations to achieve technically accurate predictions of infrared spectra. In particular, the methodology allows one to predict infrared spectra for complex systems at finite temperatures. The method’s effectiveness is demonstrated in challenging scenarios, such as the analysis of water and the organic-inorganic halide perovskite MAPbI3, where our results consistently align with experimental data. A distinctive feature of the methodology is the incorporation of derivative learning, which proves indispensable for obtaining accurate polarization data in bulk materials and facilitates the training of a machine learning surrogate model of the polarization adapted to rotational and translational symmetries. We achieve polarization prediction accuracies of about 1% for the water dimer by training only on the predicted Born effective charges.

Organisation(s)
Computational Materials Physics
External organisation(s)
VASP Software GmbH
Journal
Journal of Chemical Physics
Volume
161
No. of pages
10
ISSN
0021-9606
DOI
https://doi.org/10.48550/arXiv.2404.19674
Publication date
08-2024
Peer reviewed
Yes
Austrian Fields of Science 2012
103018 Materials physics, 102019 Machine learning, 103029 Statistical physics
ASJC Scopus subject areas
General Physics and Astronomy, Physical and Theoretical Chemistry
Portal url
https://ucrisportal.univie.ac.at/en/publications/8e434812-58a3-485f-8bc5-33a29f75486d