Doping limits in p-type oxide semiconductors

The ability to dope a semiconductor depends on whether the Fermi level can be moved into its valence or conduction bands, on an energy scale referred to the vacuum level. For oxides, there are various suitable n-type oxide semiconductors, but there is a marked absence of similarly suitable p-type oxides. This problem is of interest not only for thin-film transistors for displays, or solar cell electrodes, but also for back-end-of-line devices for the semiconductor industry. This has led to a wide-ranging search for p-type oxides using high-throughput calculations. We note that some proposed p-type metal oxides have cation s-like lone pair states. The defect energies of some of these oxides were calculated in detail. The example SnTa2O6 is of interest, but others have structures more closely based on perovskite structure and are found to have more n-type than p-type character. Graphic abstract

Machine Learning Based Methodology to Predict Point Defect Energies in Multi-Principal Element Alloys

Multi-principal element alloys (MPEAs) are a new class of alloys that consist of many principal elements randomly distributed on a crystal lattice. The random presence of many elements lends large variations in the point defect formation and migration energies even within a given alloy composition. Compounded by the fact that there could be exponentially large number of MPEA compositions, there is a major computational challenge to capture complete point-defect energy phase-space in MPEAs. In this work, we present a machine learning based framework in which the point defect energies in MPEAs are predicted from a database of their constituent binary alloys. We demonstrate predictions of vacancy migration and formation energies in face centered cubic ternary, quaternary and quinary alloys in Ni-Fe-Cr-Co-Cu system. A key benefit of building this framework based on the database of binary alloys is that it enables defect-energy predictions in alloy compositions that may be unearthed in future. Furthermore, the methodology enables identifying the impact of a given alloying element on the defect energies thereby enabling design of alloys with tailored defect properties.

Automated discovery of a robust interatomic potential for aluminum

Machine learning, trained on quantum mechanics (QM) calculations, is a powerful tool for modeling potential energy surfaces. A critical factor is the quality and diversity of the training dataset. Here we present a highly automated approach to dataset construction and demonstrate the method by building a potential for elemental aluminum (ANI-Al). In our active learning scheme, the ML potential under development is used to drive non-equilibrium molecular dynamics simulations with time-varying applied temperatures. Whenever a configuration is reached for which the ML uncertainty is large, new QM data is collected. The ML model is periodically retrained on all available QM data. The final ANI-Al potential makes very accurate predictions of radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. We perform a 1.3M atom shock simulation and show that ANI-Al force predictions shine in their agreement with new reference DFT calculations.

Band gap engineering of Ce-doped anatase TiO2 through solid solubility mechanisms and new defect equilibria formalism

Experimental data for Ce-doped TiO2 are interpreted through solubility mechanisms, structural analogies, defect energies, and a new defect equilibria formalism.