2024/09/24 by Viktor C. Birschitzky, Luca Leoni, Birschitzky, Viktor C. +5 · 2 citations
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Nuclear Physics and Applications #Physics of Superconductivity and Magnetism
paper · pdf · doi:10.48550/arxiv.2409.16179
openalex publication_date 2024/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 (FPMD). 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 FPMD 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.