Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers. These have led to the successful enhancement of their stability, a feature that is often counterbalanced by a reduction of their power-conversion efficiency. In order to provide a systematic analysis of the structure–property relationships of this class of compounds we have performed density functional theory calculations exploring fully inorganic ABC₃ chalcogenide (I–V–VI₃), halide (I–II–VII₃) and hybrid perovskites. Special attention has been given to structures featuring three-dimensional BC₆ octahedral networks because of their efficient carrier transport properties. In particular we have carefully analyzed the role of BC₆ octahedral deformations, rotations and tilts in the thermodynamic stability and optical properties of the compounds. By using machine learning algorithms we have estimated the relations between the octahedral deformation and the bandgap, and established a similarity map among all the calculated compounds.

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.

Mixing cations has been a successful strategy in perovskite synthesis by solution-processing, delivering improvements in the thermodynamic stability as well as in the lattice parameter control. Unfortunately, the relation between a given cation mixture and the associated structural deformation is not well-established, a fact that hinders an adequate identification of the optimum chemical compositions. Such difficulty arises since local distortion and microscopic disorder influence structural stability and also determine phase segregation. Hence, the search for an optimum composition is currently based on experimental trial and error, a tedious and high-cost process. Here, we report on a machine-learning-reinforced cubic-phase-perovskite stability predictor that has been constructed over an extensive dataset of first-principles calculations. Such a predictor allows us to determine the cubic phase stability at a given cation mixture regardless of the various cations’ pair and concentration, even assessing very dilute concentrations, a notoriously challenging task for first-principles calculations. In particular, we construct machine learning models, predicting multiple target quantities such as the enthalpy of mixing and various octahedral distortions. It is then the combination of these targets that guide the laboratory synthesis. Our theoretical analysis is also validated by the experimental synthesis and characterization of methylammonium–dimethylammonium-mixed perovskite thin films, demonstrating the ability of the stability predictor to drive the chemical design of this class of materials.