Predicting defects and doping of semiconductors - progress toward high-throughput calculations and relevance to materials discovery

To be useful semiconductors need to be doped. The ability to be doped (or dopability) is a phenomenon that goes beyond the dissolution of aliovalent elements as the introduced charge carriers may reside in localized (deep) defect states or could be compensated by the intrinsic point defects. As a consequence, many material systems exhibiting finite electronic band gaps are not dopable at all or allow only one charge carrier type. Hence, predicting dopability and the existence of effective dopants needs to be an integral part of any functional (semiconductor) materials discovery effort. While modern defect theory and defect calculations allow for relatively accurate assessment of the intrinsic defect chemistry, doping limits and effective dopants, the calculations themselves are demanding, requiring significant human time and computational resources, and are hence, challenging to perform in a high-throughput fashion. In this talk I will review and discuss our efforts in automating defect calculations with goal of making them an integral part of the high-throughput materials discovery workflows. Our pylada python environment together with the pylada-defects module offers automatic generation of defects structures, automated job scheduling and control, as well as post-processing of the results including application of the finite size corrections and calculations of the defect formation energies. I will also present our recent searches for wide gap semiconductors for power applications and semiconductor-impurity qubits where defect calculations and dopability (and doping) assessment are of utmost importance.