Machine learning multiscale simulation of photoconductivity in correlated oxides

预测、解释和建模量子材料的新行为需要理论洞察和最先进的多尺度建模相结合。在复杂氧化物的情况下，既表现出强烈的电子相关性，又表现出各种延伸和点缺陷，传统的电子结构方法在试图对诸如光导和体光伏效应等关键性质进行建模时遇到了严峻的挑战。幸运的是，机器学习的原子间相互作用势的非凡速度和力量为洞察这些系统提供了一种全新的方式。这个项目将设计和建立多尺度模型，以了解SrTiO3的光电导，特别是与位错核相关的增强。

Predicting, explaining and modelling novel behaviours of quantum materials requires a combination of theoretical insight with state-of-the-art multiscale modelling. In the case of complex oxides, displaying both strong electronic correlation and a diverse range of extended and point defects, traditional electronic structure methods encounter severe challenges when trying to model key properties such as photoconductivity and bulk photovoltaic effects. Fortunately, the extraordinary speed and power of machine-learned interatomic potentials provides a brand-new way to gain insight into these systems. This project will design and build multiscale models to understand photoconductivity in SrTiO3, particularly enhancement associated with dislocation cores.