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Recent first-principles approaches to semiconductors are reviewed, with an emphasis on theoretical insight into emerging materials and in silico exploration of as-yet-unreported materials. As relevant theory and methodologies have developed, along with computer performance, it is now feasible to predict a variety of material properties ab initio at the practical level of accuracy required for detailed understanding and elaborate design of semiconductors; these material properties include (i) fundamental bulk properties such as band gaps, effective masses, dielectric constants, and optical absorption coefficients; (ii) the properties of point defects, including native defects, residual impurities, and dopants, such as donor, acceptor, and deep-trap levels, and formation energies, which determine the carrier type and density; and (iii) absolute and relative band positions, including ionization potentials and electron affinities at semiconductor surfaces, band offsets at heterointerfaces between dissimilar semiconductors, and Schottky barrier heights at metalsemiconductor interfaces, which are often discussed systematically using band alignment or lineup diagrams. These predictions from first principles have made it possible to elucidate the characteristics of semiconductors used in industry, including group IIIV compounds such as GaN, GaP, and GaAs and their alloys with related Al and In compounds; amorphous oxides, represented by InGaZnO; transparent conductive oxides (TCOs), represented by In 2O 3, SnO 2, and ZnO; and photovoltaic absorber and buffer layer materials such as CdTe and CdS among group IIVI compounds and chalcopyrite CuInSe 2, CuGaSe 2, and CuIn 1 x Ga x Se 2 (CIGS) alloys, in addition to the prototypical elemental semiconductors Si and Ge. Semiconductors attracting renewed or emerging interest have also been investigated, for instance, divalent tin compounds, including SnO and SnS; wurtzite-derived ternary compounds such as ZnSnN 2 and CuGaO 2; perovskite oxides such as SrTiO 3 and BaSnO 3; and organicinorganic hybrid perovskites, represented by CH 3NH 3PbI 3. Moreover, the deployment of first-principles calculations allows us to predict the crystal structure, stability, and properties of as-yet-unreported materials. Promising materials have been explored via high-throughput screening within either publicly available computational databases or unexplored composition and structure space. Reported examples include the identification of nitride semiconductors, TCOs, solar cell photoabsorber materials, and photocatalysts, some of which have been experimentally verified. Machine learning in combination with first-principles calculations has emerged recently as a technique to accelerate and enhance in silico screening. A blend of computation and experimentation with data science toward the development of materials is often referred to as materials informatics and is currently attracting growing interest.

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