Machine Learning Small Polaron Dynamics
- Author(s)
- Viktor C. Birschitzky, Luca Leoni, Michele Reticcioli, Cesare Franchini
- Abstract
Polarons are crucial for charge transport in semiconductors, significantly impacting material properties and device performance. The dynamics of small polarons can be investigated using first-principles molecular dynamics. However, the limited timescale of these simulations presents a challenge for adequately sampling infrequent polaron hopping events. Here, we introduce a message-passing neural network combined with first-principles molecular dynamics within the Born-Oppenheimer approximation that learns the polaronic potential energy surface by encoding the polaronic state, allowing for simulations of polaron hopping dynamics at the nanosecond scale. By leveraging the statistical significance of the long timescale, our framework can accurately estimate polaron (anisotropic) mobilities and activation barriers in prototypical polaronic oxides across different scenarios (hole polarons in rocksalt MgO and electron polarons in pristine and F-doped rutile TiO2) within experimentally measured ranges.
- Organisation(s)
- Computational Materials Physics
- External organisation(s)
- University of Bologna
- Journal
- Physical Review Letters
- Volume
- 134
- No. of pages
- 8
- ISSN
- 0031-9007
- DOI
- https://doi.org/10.48550/arXiv.2409.16179
- Publication date
- 05-2025
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103018 Materials physics, 102019 Machine learning
- ASJC Scopus subject areas
- General Physics and Astronomy
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/2fed90b0-6a3b-4daa-9e07-ac647dffcedc