Polarons are charged quasiparticles that form in polarizable materials. These particles play a significant role in important phenomena such as charge transfer, electron-hole recombination, and catalytic processes. Different methods have been used to study the equilibrium properties of these states while their dynamics continues to be accessible only through standard ab initio molecular dynamics (AIMD). However, the limited time scales and system sizes of such simulations makes the adequate sampling of infrequent polaron hopping events a challenge. In recent years, machine learning potentials (MLPs) have helped bridge the gap between time- and length-scales in AIMD. In this study, we develop a MLPs architecture using an equivariant graph neural network that successfully accounts for small polaron hopping dynamics at the nanosecond timescale. We apply the proposed ML framework to study hole and electron polaron dynamics in MgO and (F-doped) TiO2, enabling the estimation of the polaron (anisotropic) mobility across varying temperatures and in the presence of dopants.
*This research was funded by the Austrian Science Fund (FWF) 10.55776/F81 project TACO. C.F. and L.L. acknowledge support by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3 - Project NEST (Network 4 Energy Sustainable Transition) and CN-HPC grant no. (CUP) J33C22001170001, SPOKE 7, of Ministero dell'Università e della Ricerca (MUR), funded by the European Union – NextGenerationEU. V.B. gratefully acknowledges funding from the Vienna Doctoral School in Physics (VDSP). The computational results presented have been achieved using the Vienna Scientific Cluster (VSC). We acknowledge access to LEONARDO at CINECA, Italy, via an AURELEO (Austrian Users at LEONARDO supercomputer) project.
–
Publication:Birschitzky, V. C., Leoni, L., Reticcioli, M. & Franchini, C. Machine Learning Small Polaron Dynamics 2024. arXiv: 2409.16179 [cond-mat.mtrl-sci]. https://arxiv.org/abs/2409.16179
