Electronic structure and density of states of the random Al0.5Ga0.5As, GaAs0.5P0.5, and Ga0.5In0.5As semiconductor alloys

Electronic Properties of Zn2V(1-x)NbxN3 Alloys to Model Novel Materials for Light-Emitting Diodes

We propose the Zn2V(1-x)NbxN3 alloy as a new promising material for optoelectronic applications, in particular for light-emitting diodes (LEDs). We perform accurate electronic-structure calculations of the alloy for several concentrations x using density-functional theory with meta-GGA exchange-correlation functional TB09. The band gap is found to vary between 2.2 and 2.9 eV with varying V/Nb concentration. This range is suitable for developing bright LEDs with tunable band gap as potential replacements for the more expensive Ga(1-x)In(x)N systems. Effects of configurational disorder are taken into account by explicitly considering all possible distributions of the metal ions within the metal sublattice for the chosen supercells. We have evaluated the band gap's nonlinear behavior (bowing) with variation of V/Nb concentration for two possible scenarios: (i) only the structure with the lowest total energy is present at each concentration and (ii) the structure with minimum band gap is present at each concentration, which corresponds to experimental conditions when also metastable structures are presents. We found that the bowing is about twice larger in the latter case. However, in both cases, the bowing parameter is found to be lower than 1 eV, which is about twice smaller than that in the widely used Ga(1-x)In(x)N alloy. Furthermore, we found that both crystal volume changes due to alloying and local effects (atomic relaxation and the V-N/Nb-N bonding difference) have important contributions to the band gap bowing in Zn2V(1-x)NbxN3.

Machine Learning Force Field Aided Cluster Expansion Approach to Configurationally Disordered Materials: Critical Assessment of Training Set Selection and Size Convergence

Cluster expansion (CE) is a powerful theoretical tool to study the configuration-dependent properties of substitutionally disordered systems. Typically, a CE model is built by fitting a few tens or hundreds of target quantities calculated by first-principles approaches. To validate the reliability of the model, a convergence test of the cross-validation (CV) score to the training set size is commonly conducted to verify the sufficiency of the training data. However, such a test only confirms the convergence of the predictive capability of the CE model within the training set, and it is unknown whether the convergence of the CV score would lead to robust thermodynamic simulation results such as order-disorder phase transition temperature T_c. In this work, using carbon defective MoC_1-x as a model system and aided by the machine-learning force field technique, a training data pool with about 13000 configurations has been efficiently obtained and used to generate different training sets of the same size randomly. By conducting parallel Monte Carlo simulations with the CE models trained with different randomly selected training sets, the uncertainty in calculated T_c can be evaluated at different training set sizes. It is found that the training set size that is sufficient for the CV score to converge still leads to a significant uncertainty in the predicted T_c and that the latter can be considerably reduced by enlarging the training set to that of a few thousand configurations. This work highlights the importance of using a large training set to build the optimal CE model that can achieve robust statistical modeling results and the facility provided by the machine-learning force field approach to efficiently produce adequate training data.

Bandgap Engineering in the Configurational Space of Solid Solutions via Machine Learning: (Mg,Zn)O Case Study

Computer simulations of alloys' properties often require calculations in a large space of configurations in a supercell of the crystal structure. A common approach is to map density functional theory results into a simplified interaction model using so-called cluster expansions, which are linear on the cluster correlation functions. Alternative descriptors have not been sufficiently explored so far. We show here that a simple descriptor based on the Coulomb matrix eigenspectrum clearly outperforms the cluster expansion for both total energy and bandgap energy predictions in the configurational space of a MgO-ZnO solid solution, a prototypical oxide alloy for bandgap engineering. Bandgap predictions can be further improved by introducing non-linearity via gradient-boosted decision trees or neural networks based on the Coulomb matrix descriptor.

Cluster expansion based configurational averaging approach to bandgaps of semiconductor alloys

Configurationally disordered semiconducting materials including semiconductor alloys [e.g., (GaN)_1-x(ZnO)_x] and stoichiometric materials with fractional occupation (e.g., LaTiO₂N) have attracted a lot of interest recently in search for efficient visible light photo-catalysts. First-principles modeling of such materials poses great challenges due to the difficulty in treating the configurational disorder efficiently. In this work, a configurational averaging approach based on the cluster expansion technique has been exploited to describe bandgaps of ordered, partially disordered (with short-range order), and fully disordered phases of semiconductor alloys on the same footing. We take three semiconductor alloys [Cd_1-xZn_xS, ZnO_1-xS_x, and (GaN)_1-x(ZnO)_x] as model systems and clearly demonstrate that semiconductor alloys can have a system-dependent short-range order that has significant effects on their electronic properties.

Structural and electronic contributions to the bandgap bowing of (In,Ga)P alloys

The variation of atomic configurations and their corresponding structural parameters, such as first nearest neighbor distances, is a characteristic feature of ternary semiconductor alloys with zincblende structure. It has a strong influence on important material properties, most prominently the bandgap energy, and can contribute to a nonlinear behavior with changing alloy composition. Using (In,Ga)P as a model system, the atomic-scale structure has been modeled for all possible first nearest neighbor configurations based on experimentally determined structural parameters. While the average position of the P anion corresponds to the ideal lattice site, the average distance from this ideal lattice site does not vanish even in the random alloy. Based on this average anion displacement, the contribution to the bandgap bowing caused by structural relaxation of the alloy was calculated over the whole compositional range. Furthermore, the bowing contribution arising from the overall change of the In-P and Ga-P distances with respect to the binary values was determined. Thus, a clear distinction between the bandgap bowing caused by structural effects on the one hand and by charge redistribution on the other hand is possible.

Element resolved spin configuration in ferromagnetic manganese-doped gallium arsenide

We report induced Ga and As moments in ferromagnetic Ga(1-x)MnxAs detected using x-ray magnetic circular dichroism at the Mn, Ga, and As L(3,2) edges. Across a broad composition range, we find As and Ga dichroism signals which indicate an As 4s moment coupled antiparallel to the Mn 3d moment, and a smaller parallel Ga 4s moment. The Ga moment follows that of Mn in both doping and temperature dependence. These results are consistent with recent predictions of induced GaAs host moments and support the model of carrier-mediated ferromagnetic ordering involving As-derived valence band states.