Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining small polarons’ spatial distributions is essential to understand materials properties and functionalities. However, the required exploration of the configurational space is computationally demanding when using first principles methods. Here, we propose a machine-learning (ML) accelerated search that determines the ground state polaronic configuration. The ML model is trained on databases of polaron configurations generated by density functional theory (DFT) via molecular dynamics or random sampling. To establish a mapping between configurations and their stability, we designed descriptors modelling the interactions among polarons and charged point defects. We used the DFT+ML protocol to explore the polaron configurational space for two surface-systems, reduced rutile TiO₂(110) and Nb-doped SrTiO₃(001). The ML-aided search proposes additional polaronic configurations and can be utilized to determine optimal polaron distributions at any charge concentration.

极化子缺陷在材料中普遍存在，并在涉及载流子迁移率、电荷转移和表面反应性的许多过程中发挥重要作用。确定小极化子的空间分布对于理解材料特性和功能至关重要。然而，当使用第一原理方法时，对配置空间的必要探索在计算上是有要求的。在这里，我们提出了一种机器学习 (ML) 加速搜索来确定基态极化子配置。ML 模型在由密度泛函理论 (DFT) 通过分子动力学或随机采样生成的极化子配置数据库上进行训练。为了建立配置及其稳定性之间的映射，我们设计了对极化子和带电点缺陷之间的相互作用进行建模的描述符。₂ (110)和Nb掺杂的SrTiO ₃ (001)。ML 辅助搜索提出了额外的极化子配置，可用于确定任何电荷浓度下的最佳极化子分布。