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 we 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.

PbTe is one of the highest-performing known thermoelectric materials. Much of its promising thermoelectric performance can be attributed to high valley degeneracy due to having a valence band minimum (VBM) and conduction band maximum (CBM) at the L-point in the first Brillouin zone, which has 4-fold degeneracy, instead of at Γ, which has 1-fold degeneracy. The existence of the VBM at L has been explained by the contribution of Pb-s states that make up the valence band edge. However, the dominance of Te-p states and the presence of Pb-p states near the VBM suggest that the Pb-s orbitals may not be as crucial as previously thought. The tight-binding (TB) or linear combination of atomic orbitals (LCAO) method of calculating electronic structures is ideally suited to gain qualitative insights to explain how simple chemistry and bonding principles lead to complex electronic structures of materials. In this study, we use a physically self-consistent TB model to understand the extent to which various atomic orbital interactions contribute to having a VBM at L instead of Γ. Based on the dominant interactions at play, a simple molecular orbital (MO) picture is developed that when extended into the periodic crystal explains the shape of the valence band dispersion between L and Γ. We find that there is sufficient interaction between Pb-p and Te-p states to provide the MO with the proper s-type symmetry to place the VBM at L rather than the usual p-type symmetry of the VB in rocksalt structures, where the VBM is at Γ. Furthermore, we show that the VBM would be at L even if the Pb-s states were removed and that the Pb-p states are at least as critical of a factor in dictating the position of the VBM in PbTe and in the other lead chalcogenides.

The recent surge of interest in K-ion batteries necessitates a fundamental understanding of phase stability and K ordering tendencies in common electrode materials. We report on a first-principles study of phase stability in layered K x CoO2 (0 ≤ x ≤ 1) in the O1/P3/O3 family of host structures and identify K ordering preferences within each host. We find that the P3 host is stable at intermediate K concentrations and exhibits a multitude of hierarchical orderings characterized by well-ordered domains separated by antiphase boundaries. We also predict the stability of a new family of layered structures at high K concentrations that are highly distorted and host both octahedrally and prismatically coordinated K within each intercalation layer.

While p-type BiCuSeO is a well-known mid-temperature oxide thermoelectric (TE) material, computations predict that superior TE performance can be realized through n-type doping. In this study, we use first-principles defect...

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