Pablo Sánchez-Palencia

Cu-based ternary oxides with delafossite structure have received considerable attention in recent years for their versatility in a wide range of applications, among which is the possibility to use them in heterostructure solar cells as hole transport layers, due to their promising behavior as p-type conducting oxides. Ab initio calculations have been performed with density functional theory to investigate the role of the trivalent metal within the CuMO₂ structure and the dependence of structural and electronic properties with the species (M = Al, Ga, In, Fe, Cr, Co, Sc, Y) occupying the site of the metal. Generalized Gradient Approximation also including a Hubbard term and nonlocal Heyd–Scuseria–Enzerhof screened hybrid functional schemes were tested and their results were compared. Excellent agreement with experimental lattice parameters and measured gaps have been found. The use of hybrid functionals in HSE approximation considerably improves the bandgaps when compared with the experimental results but takes considerable time to converge, hence the need to explore less demanding methodologies. Trends in the geometry as well as in the electronic properties are discussed, and the effect of mixing different metals (CuM_xN_1−xO₂, M, N being the aforementioned elements) in the geometry and electronic properties of these delafossite materials is investigated. Due to the high cost of HSE calculations, especially when supercells are needed to model several x concentrations, statistical models and techniques based on machine learning have also been explored to predict HSE bandgap values from GGA and structural information.

Ab initio prediction of the variation of properties in the configurational space of solid solutions is computationally very demanding. We present an approach to accelerate these predictions via a combination of density functional theory and machine learning, using the cubic spinel nitride GeSn₂N₄ as a case study, exploring how formation energy and electronic bandgap are affected by configurational variations. Furthermore, we demonstrate the utility of applying explainable machine learning to understand the crystal chemistry origins of the trends that we observe. Different configuration descriptors (Coulomb matrix eigenspectrum, many-body tensor representation, and cluster correlation function vectors) are combined with different models (linear regression, gradient-boosted decision trees, and multi-layer perceptron) to extrapolate the calculation of ab initio properties from a small set of configurations to the full space with thousands of configurations. We discuss the performance of different descriptors and models. SHAP (SHapley Additive exPlanations) analysis of the machine learning models highlights how values of formation energy are dominated by variations in local crystal structure (single polyhedral environments), while values of electronic bandgap are dominated by variations in more extended structural motifs. Finally, we demonstrate the usefulness of this approach by constructing structure–property maps, identifying important configurations of GeSn₂N₄ with extremal properties, as well as by calculating accurate equilibrium properties using configurational averaging.

All-inorganic Rb _a Cs _{1− a} Sn _b Pb _{1− b} I ₂ Br (1 > b > 0.5) perovskites would be adequate candidates for photovoltaic applications with improved stability and reduced Pb concentration.

A systematicab-initiostudy of all-inorganic perovskites with formula CsPb_1−bSn_b(I_1−xBr_x)₃ has been performed, elucidating the connection of that composition with their structural, thermodynamics and mechanical properties.

This work explores the possibility of increasing the photovoltaic efficiency of InP semiconductors through a hyperdoping process with transition metals (TM = Ti, V, Cr, Mn). To this end, we investigated the crystal structure, electronic band and optical absorption features of TM-hyperdoped InP (TM@InP), with the formula TMxIn1-xP (x = 0.03), by using accurate ab initio electronic structure calculations. The analysis of the electronic structure shows that TM 3d-orbitals induce new states in the host semiconductor bandgap, leading to improved absorption features that cover the whole range of the sunlight spectrum. The best results are obtained for Cr@InP, which is an excellent candidate as an in-gap band (IGB) absorber material. As a result, the sunlight absorption of the material is considerably improved through new sub-bandgap transitions across the IGB. Our results provide a systematic and overall perspective about the effects of transition metal hyperdoping into the exploitation of new semiconductors as potential key materials for photovoltaic applications.