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Interplay between Composition, Electronic Structure, Disorder, and Doping due to Dual Sublattice Mixing in Nonequilibrium Synthesis of ZnSnN <sub/>2</sub> :O
摘要： The opportunity for enhanced functional properties in semiconductor solid solutions has attracted vast scientific interest for a variety of novel applications. However, the functional versatility originating from the additional degrees of freedom due to atomic composition and ordering comes along with new challenges in characterization and modeling. Developing predictive synthesis–structure–property relationships is prerequisite for effective materials design strategies. Here, a first-principles based model for property prediction in such complex semiconductor materials is presented. This framework incorporates nonequilibrium synthesis, dopants and defects, and the change of the electronic structure with composition and short range order. This approach is applied to ZnSnN2 (ZTN) which has attracted recent interest for photovoltaics. The unintentional oxygen incorporation and its correlation with the cation stoichiometry leads to the formation of a solid solution with dual sublattice mixing. A nonmonotonic doping behavior as a function of the composition is uncovered. The degenerate doping of near-stoichiometric ZTN, which is detrimental for potential applications, can be lowered into the 1017 cm?3 range in highly off-stoichiometric material, in quantitative agreement with experiments.
关键词： multinary material,computational materials design,disorder,semiconductor solid solution,electronic structure,nonequilibrium synthesis,defects
更新于2025-09-19 17:15:36
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Silicate-germanates with the doubled mica-like layers [Pb(Si,Ge)O4]2-∞∞ in centrosymmetric Rb2Pb2[(Ge0.33Si0.67)2O7] and isolated in acentric, optically nonlinear Li6Pb2[(Ge0.4Si0.6)O4]2(OH)2
摘要： Silicate-germanates, Rb2Pb2[(Si0.67Ge0.33)2O7], P m1, and Li6Pb2[(Ge0.4Si0.6)O4]2(OH)2, P63mc, were synthesized in the multi-component hydrothermal systems. According to single crystal XR data, both structures contain identical mica-like layers [Pb(Ge,Si)O4]2- ∞∞, double or single, comprised of [(Si,Ge)O4]4--tetrahedra and umbrella-like groups [PbO3]4-: the latter play a unique role of (Si,Ge)-tetrahedra being of the same charge, dimension and the Pb-lone-pair as the forth apex. Predicted earlier polar ortho-silicate structure appears with small Li atom, while the centrosymmetric diortho-silicate structure type A2Pb2[B2O7] is stable with large A = K, Rb, Cs. Layers with {Li3O7}11- atomic clusters separate single tetrahedral layers. Under Nd:YAG laser illumination the powder of acentric Li,Pb-silicate-germanate yields intensive light of second harmonic. The maximum second harmonic intensity 10 times more in comparison with quartz standard is achieved in the powder with a grain size of about 20 μm. The space-averaged second-order nonlinear coefficient <d> = 1.1 pm/V. The comparably high optical nonlinearity is connected with the one-side directed umbrella-like polar units [PbO3]4- in polar structure of the Li,Pb-silicate-germanate.
关键词： materials design,structure,hydrothermal synthesis,lead-silicate-germanate,structure-properties relations,optical nonlinearity
更新于2025-09-16 10:30:52
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Rationalizing Perovskites Data for Machine Learning and Materials Design
摘要： Machine learning has been recently used for novel perovskite designs, owing to the availability of large amount of perovskite formability data. Trustworthy results should be based on the valid and reliable data that can reveal the nature of materials as much as possible. In this study, a procedure has been developed to identify the formability of perovskites for all the compounds with the stoichiometry of ABX3 and (A′A′′)(B′B′′)X6, that exist in experiments and are stored in the database of Materials Projects. Our results have enriched data of perovskite formability in a large extent and corrected the possible errors of previous data in ABO3 compounds. Furthermore, machine learning with multiple models approach have identified the A2B′B′′O6 compounds that have suspicious formability results in current experimental data. Therefore, further experimental validation experiments are called for. This work paves a way for cleaning perovskite formability data for reliable machine learning work in future.
关键词： Perovskites,Energy Conversion and Storage,Machine Learning,Plasmonics and Optoelectronics,Materials Design
更新于2025-09-09 09:28:46
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Mix and Match: Organic and Inorganic Ions in the Perovskite Lattice
摘要： Materials science evolves to a state where the composition and structure of a crystal can be controlled almost at will. Given that a composition meets basic requirements of stoichiometry, steric demands, and charge neutrality, researchers are now able to investigate a wide range of compounds theoretically and, under various experimental conditions, select the constituting fragments of a crystal. One intriguing playground for such materials design is the perovskite structure. While a game of mixing and matching ions has been played successfully for about 150 years within the limits of inorganic compounds, the recent advances in organic–inorganic hybrid perovskite photovoltaics have triggered the inclusion of organic ions. Organic ions can be incorporated on all sites of the perovskite structure, leading to hybrid (double, triple, etc.) perovskites and inverse (hybrid) perovskites. Examples for each of these cases are known, even with all three sites occupied by organic molecules. While this change from monatomic ions to molecular species is accompanied with increased complexity, it shows that concepts from traditional inorganic perovskites are transferable to the novel hybrid materials. The increased compositional space holds promising new possibilities and applications for the universe of perovskite materials.
关键词： hybrid perovskites,inverse hybrid perovskites,materials design,photovoltaic,DFT calculations
更新于2025-09-04 15:30:14