We present a computational screening study of ternary metal borohydrides for reversible hydrogen storage based on density functional theory. We investigate the stability and decomposition of alloys containing 1 alkali metal atom, Li, Na, or K ͑M 1 ͒; and 1 alkali, alkaline earth or 3d / 4d transition metal atom ͑M 2 ͒ plus two to five ͑BH 4 ͒ − groups, i.e., M 1 M 2 ͑BH 4 ͒ 2-5 , using a number of model structures with trigonal, tetrahedral, octahedral, and free coordination of the metal borohydride complexes. Of the over 700 investigated structures, about 20 were predicted to form potentially stable alloys with promising decomposition energies. The M 1 ͑Al/ Mn/ Fe͒͑BH 4 ͒ 4 , ͑Li/ Na͒Zn͑BH 4 ͒ 3 , and ͑Na/ K͒͑Ni/ Co͒͑BH 4 ͒ 3 alloys are found to be the most promising, followed by selected M 1 ͑Nb/ Rh͒͑BH 4 ͒ 4 alloys.

Forecasting the structural stability of hybrid organic/inorganic compounds, where polyatomic molecules replace atoms, is a challenging task; the composition space is vast, and the reference structure for the organic molecules is ambiguously defined. In this work, we use a range of machine-learning algorithms, constructed from state-of-the-art density functional theory data, to conduct a systematic analysis on the likelihood of a given cation to be housed in the perovskite structure. In particular, we consider both ABC3 chalcogenide (I–V–VI3) and halide (I–II–VII3) perovskites. We find that the effective atomic radius and the number of lone pairs residing on the A-site cation are sufficient features to describe the perovskite phase stability. Thus, the presented machine-learning approach provides an efficient way to map the phase stability of the vast class of compounds, including situations where a cation mixture replaces a single A-site cation. This work demonstrates that advanced electronic structure theory combined with machine-learning analysis can provide an efficient strategy superior to the conventional trial-and-error approach in materials design.

Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers.

Data-driven approaches for materials design and selection have accelerated materials discovery along with the upsurge of machine learning applications. We report here a prediction-to-lab-scale synthesis of cubic phase triple-cation lead halide perovskites guided by a machine learning perovskite stability predictor. The starting double-cation perovskite resulting from the incorporation of 15% dimethylammonium (DMA) in methylammonium lead triiodide suffers from significant deviation from the perovskite structure. By analyzing the X-ray diffraction and scanning electron microscopy, we confirmed that it is possible to recover the perovskite structure with the cubic phase at room temperature (RT) while minimizing the iterations of trial-and-error by adding <10 mol % of cesium cation additives, as guided by the machine learning predictor. Our conclusions highly support the cubic-phase stabilization at RT by controlling the stoichiometric ratio of various sized cations. This prediction-to-lab-scale synthesis approach also enables us to identify room for improvements of the current machine learning predictor to take into consideration the cubic phase stability as well as phase segregation.

Urocortin 3 (Ucn 3), member of the corticotropin-releasing factor (CRF) family of peptide hormones, is released from β-cells to potentiate insulin secretion. Ucn 3 activates the CRF type-2 receptor (CRFR2) but does not activate the type-1 receptor (CRFR1), which was recently demonstrated on β-cells. While the direct actions of Ucn 3 on insulin secretion suggest the presence of cognate receptors within the islet microenvironment, this has not been established. Here we demonstrate that CRFR2α is expressed by MIN6 insulinoma cells and by primary mouse and human islets, with no detectable expression of CRFR2β. Furthermore, stimulation of MIN6 cells or primary mouse islets in vitro or in vivo with glucocorticoids (GCs) robustly and dose-dependently increases the expression of CRFR2α, while simultaneously inhibiting the expression of CRFR1 and incretin receptors. Luciferase reporters driven by the mouse CRFR1 or CRFR2α promoter in MIN6 cells confirm these differential effects of GCs. In contrast, GCs inhibit CRFR2α promoter activity in HEK293 cells and inhibit the expression of CRFR2β in A7r5 rat aortic smooth muscle cells and differentiated C2C12 myotubes. These findings suggest that the GC-mediated increase of CRFR2α depends on the cellular context of the islet and deviates from the GC-mediated suppression of CRFR1 and incretin receptors. Furthermore, GC-induced increases in CRFR2α expression coincide with increased Ucn 3-dependent activation of cAMP and MAPK pathways. We postulate that differential effect of GCs on the expression of CRFR1 and CRFR2α in the endocrine pancreas represent a mechanism to shift sensitivity from CRFR1 to CRFR2 ligands.

The intramolecular magnetic exchange coupling of edge terminated zigzag graphene nanoribbon (ZGNR) was studied with density functional theory calculations. In order to examine the applicability of the spin alternation rule and a classification scheme for radicals and couplers on functionalized graphene nanoribbons, we investigated the magnetic behaviors of pristine zigzag graphene nanoribbon with eight zigzag chains (8-ZGNR) and 8-ZGNRs terminated with trimethylenemethane (TMM) and 6-oxoverdazyl (OVER) radicals,that is, TMM-ZGNR-TMM (TZT), OVER-ZGNR-OVER (OZO), and TMM-ZGNR-OVER (TZO). As expected, only ZGNR terminated with different group radicals on each edge (TZO) had a ferromagnetic (high-spin) ground state with an energy gap of 39 meV/supercell (321.57 cm −1 ) relative to the low-spin state. This strongly supports the validity of the spin alternation rule and the classification scheme for radicals and couplers on extensively conjugated large graphene nanoribbons. TZT and OZO were found to have an antiferromagnetic (low-spin) ground state with magnetic coupling weaker than that of interedge antiferromagnetic superexchange of pristine 8-ZGNR. Based on the spin distribution pattern on magnetic ground states, GNR prefers to have each edge in antiferromagnetic order, which satisfies Lieb's theorem on the Hubbard model and spin alternation rule. All of the terminated ZGNRs exhibited semiconducting properties with an energy gap of 0.06−0.21 eV.

In the quest for nontoxic and stable perovskites for solar cells, we have conducted a systematic study of the effect of chalcogen content in oxychalcogenide perovskite by using DFT and quasi-particle perturbation theory. We explored the changes in the electronic structure due to the substitution of O atoms in NaNbO and NaTaO perovskite structures with various chalcogens (S, Se, Te) at different concentrations. Interestingly, the introduction of the chalcogen atoms resulted in a drastic reduction in the electronic band gap, which made some of the compounds fall within the visible range of the solar spectrum. In addition, our analysis of the electronic structure shows that the optical transition becomes direct as a result of the strong hybridization between the orbitals of the transition metal and those of the chalcogen ion, in contrast to the indirect band feature of NaNbO and NaTaO . We identified candidates with a high theoretical solar conversion efficiency that approached the Shockley-Queisser limit, which makes them suitable for thin-film solar cell applications. The present work serves as a guideline for experimental efforts by identifying the chalcogen content that should be targeted during the synthetic route of thermodynamically stable and strongly photoactive absorbers for oxychalcogenide perovskites in thin-film solar cells.

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