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.
Monsieur Natalio Mingo (Laboratoire d'Innovation pour les Technologies des Energies nouvelles et les Nanomatériaux)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
TU WIEN Institute for Materials Chemistry
LITEN Laboratoire d'Innovation pour les Technologies des Energies nouvelles et les Nanomatériaux
Help of the ANR 203,952 euros
Beginning and duration of the scientific project: - 36 Months