Machine learning multiscale simulation of photoconductivity in correlated oxides

相关氧化物光电导性的机器学习多尺度模拟

• 批准号: 2887639
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887639
• 关键词: Machine; learning; multiscale; simulation; photoconductivity

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.

预测、解释和建模量子材料的新行为需要将理论洞察力与最先进的多尺度建模相结合。在复杂氧化物的情况下，显示出强的电子相关性和各种各样的扩展和点缺陷，传统的电子结构方法遇到了严峻的挑战，当试图模拟关键性能，如光电导性和体光伏效应。幸运的是，机器学习原子间相互作用势的非凡速度和力量为深入了解这些系统提供了一种全新的方式。该项目将设计和构建多尺度模型，以了解SrTiO 3的光电导性，特别是与位错核心相关的增强。