Automated Modeling of Polarons: Defects and Reactivity on TiO$_2$(110) Surfaces

TL;DR

This paper introduces an automated DFT workflow combined with machine learning to efficiently model polarons and defects on TiO₂ surfaces, revealing how oxygen vacancies influence surface reactivity and charge trapping.

Contribution

The work presents a novel automated and machine learning-accelerated method for identifying polaron configurations in complex materials, enabling large-scale studies of defect-polaron interactions.

Findings

Nb doping minimally affects reactivity with CO adsorbates

Oxygen vacancies significantly influence surface reactivity

Polaron stabilization depends on local defect arrangements

Abstract

Polarons are widespread in functional materials and are key to device performance in several technological applications. However, their effective impact on material behavior remains elusive, as condensed matter studies struggle to capture their intricate interplay with atomic defects in the crystal. In this work, we present an automated workflow for modeling polarons within density functional theory (DFT). Our approach enables a fully automatic identification of the most favorable polaronic configurations in the system. Machine learning techniques accelerate predictions, allowing for an efficient exploration of the defect-polaron configuration space. We apply this methodology to Nb-doped TiO$_2$(110) surfaces, providing new insights into the role of defects in surface reactivity. Using CO adsorbates as a probe, we find that Nb doping has minimal impact on reactivity, whereas oxygen vacancies contribute significantly depending on their local arrangement via the stabilization of polarons on the surface atomic layer. Our package streamlines the modeling of charge trapping and polaron localization with high efficiency, enabling systematic, large-scale investigations of polaronic effects across complex material systems.