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Machine Learning Small Polaron Dynamics

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arxiv 2409.16179 v2 pith:7LQ32HOY submitted 2024-09-24 cond-mat.mtrl-sci

classification cond-mat.mtrl-sci
keywords dynamicspolaronpolaronspolaronicfpmdhoppingsimulationssmall
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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 (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 TiO$_2$) within experimentally measured ranges.

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