用于探索小极化子构型空间的机器学习方法
Machine Learning for Exploring Small Polaron Configurational Space
AI总结：
提出一种基于核回归的机器学习加速搜索方法，结合DFT计算数据与极化子相互作用描述符，高效探索多极化子构型空间，准确识别基态模式并确定任意电荷浓度下的最优极化子分布。
摘要：
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性质和功能至关重要。这需要探索构型空间，使用标准第一性原理方法时计算量巨大，且对于多极化子系统在技术上不可行。在此，我们提出了一种机器学习（ML）加速搜索方法，用于比较不同极化子模式的能量稳定性并确定基态构型。基于核回归的ML模型在密度泛函理论（DFT）计算生成的数据库上进行训练，该数据库基于通过分子动力学模拟或随机采样方法获得的一组最小初始极化子模式构建。为了在训练数据和构型稳定性之间建立有效的映射，我们设计了简单的描述符来模拟极化子与带电点缺陷之间的相互作用。本文提出的DFT+ML协议被用于探索两个不同系统的数百万种极化子构型：氧缺陷金红石型TiO$_2$(110)和Nb掺杂SrTiO$_3$(001)。我们的数据表明，ML辅助搜索能够正确识别基态极化子模式，提出训练中未涉及的极化子构型，并可用于有效确定任意电荷浓度下极化子的最优分布。
英文摘要：
Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining the spatial distribution of small polarons is essential to understand materials properties and functionalities. This requires an exploration of the configurational space, which is computationally demanding when using standard first principles methods, and technically prohibitive for many-polaron systems. Here, we propose a machine-learning (ML) accelerated search that compares the energy stability of different polaron patterns and determines the ground state configuration. The kernel-regression based ML model is trained on databases generated by density functional theory (DFT) calculations on a minimal set of initial polaron patterns, obtained by using either molecular dynamics simulations or a random sampling approach. To establish an efficient mapping between training data and configuration stability we designed simple descriptors that model the interactions among polarons and charged point defects. The proposed DFT+ML protocol is used here to explore millions of polaron configurations for two different systems, oxygen defective rutile TiO$_2$(110) and Nb-doped SrTiO$_3$(001). Our data shows that the ML-aided search correctly individuates the ground-state polaron patterns, proposes polaronic configurations not visited in the training and can be used to efficiently determine the optimal distribution of polarons at any charge concentration.
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