Defect-induced functionality in complex oxides

This project will establish fundamental physical design rules for defect-induced functional materials for energy conversion and energy-efficient electronics. Both the production of clean energy as well as the development of novel, energy-efficient IT components are among the currently most pressing societal challenges.

Defects are a large yet mostly uncharted space in computational material design. There is a lack of predictive understanding of the role of defects in complex oxides due to the many competing or cooperating instabilities in these materials. The project will establish a fundamental scientific understanding of the role of defects via a combination of density functional theory (DFT) calculations and a descriptor-based machine-learning analysis.

Building the systematic database required as input for machine learning is a computationally expensive task, impossible without access to international high-performance computing (HPC) resources. The ultimate goal of this project is to put defect-induced functionality in complex oxides on the same predictive footing that has powered the success of the semiconductor industry for decades.