  The ability to predict the likelihood of impurity incorporation and their
electronic energy levels in semiconductors is crucial for controlling its
conductivity, and thus the semiconductor's performance in solar cells,
photodiodes, and optoelectronics. The difficulty and expense of experimental
and computational determination of impurity levels makes a data-driven machine
learning approach appropriate. In this work, we show that a density functional
theory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe,
and CdS can lead to accurate and generalizable predictive models of defect
properties. By converting any semiconductor + impurity system into a set of
numerical descriptors, regression models are developed for the impurity
formation enthalpy and charge transition levels. These regression models can
subsequently predict impurity properties in mixed anion CdX compounds (where X
is a combination of Te, Se and S) fairly accurately, proving that although
trained only on the end points, they are applicable to intermediate
compositions. We make machine-learned predictions of the Fermi-level dependent
formation energies of hundreds of possible impurities in 5 chalcogenide
compounds, and suggest a list of impurities which can shift the equilibrium
Fermi level in the semiconductor as determined by the dominant intrinsic
defects. These dominating impurities as predicted by machine learning compare
well with DFT predictions, revealing the power of machine-learned models in the
quick screening of impurities likely to affect the optoelectronic behavior of
semiconductors.
