Machine Learning Small Polaron Dynamics

Autor(en)

Viktor C. Birschitzky, Luca Leoni, Michele Reticcioli, Cesare Franchini

Abstrakt

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(en)

Computergestützte Materialphysik

Externe Organisation(en)

Università di Bologna

Journal

Physical Review Letters

Band

134

Anzahl der Seiten

8

ISSN

0031-9007

DOI

https://doi.org/10.48550/arXiv.2409.16179

Publikationsdatum

05-2025

Peer-reviewed

Ja

ÖFOS 2012

103018 Materialphysik, 102019 Machine Learning

ASJC Scopus Sachgebiete

Allgemeine Physik und Astronomie

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/2fed90b0-6a3b-4daa-9e07-ac647dffcedc