High-throughput Computation of Defects in Semiconductors – CODIS

We propose a large-scale computational screening of defects in semiconductors. The project will be the first to inter-relate growth conditions, defect types and concentrations, and functional transport properties. The study will combine several approaches developed by the two project partners and streamline them into an automated flow linked to a material-defects database. Innovative machine learning classification and regression algorithms will be applied to unveil hidden trends and relationships and to accelerate the screening process. A novel understanding of semiconductors according to their prevalent defect types will thus emerge. Specific focus will be put on the lattice thermal conductivity to elucidate the characteristic relationships between the defects and the materials functional properties. In addition to its fundamental interest, this project will impact many industrial technologies where semiconductor defects are key to their performance.