Structural, Optical, and Electronic Properties of Wide Bandgap Perovskites: Experimental and Theoretical Investigations

Wide bandgap hybrid halide perovskites based on bromine and chlorine halide anions have emerged as potential candidates for various optoelectronic devices. However, these materials are relatively less explored than the iodine-based perovskites for microscopic details. We present experiment and first-principles calculations to understand the structural, optical, and electronic structure of wide bandgap CH₃NH₃Pb­(Br_1–<i>x</i>Cl_<i>x</i>)₃ (<i>x</i> = 0, 0.33, 0.66, and 1) 3D hybrid perovskite materials. We substituted Br^– with Cl^– to tune the bandgap from 2.4 eV (green emissive) to 3.2 eV (blue (UV) emissive) of these materials. We correlate our experimental results with first-principles theory and provide an insight into important parameters like lattice constants, electronic structure, excitonic binding energy (<i>E</i>_X), dielectric constant, and reduced effective mass (μ_r) of charge carriers in these perovskite semiconductors. Electronic structure calculations reveal that electronic properties are mainly governed by Pb 6p and halide p orbitals. Our estimates of <i>E</i>_X within a hydrogen model suggest that an increase in <i>E</i>_X by increasing the Cl^– (chlorine) concentration is mainly due to a decrease in the dielectric constant with <i>x</i> and almost constant value of μ_r close to the range of 0.07<i>m</i>_e

Is There a Lower Size Limit for Superconductivity?

The ultimate lower size limit for superconducting order to exist is set by the “Anderson criterion”arising from quantum confinementthat appears to be remarkably accurate and universal. We show that carefully grown, phase-pure, nanocrystalline <i>bcc</i>-Ta remains superconducting (with ordering temperature, <i>T</i>_C ≈ 0.9 K) down to sizes 40% below the conventional estimate of the Anderson limit of 4.0 nm. Further, both the <i>T</i>_C and the critical magnetic field exhibit an unusual, nonmonotonic size dependence, which we explain in terms of a complex interplay of quantum size effects, surface phonon softening, and lattice expansion. A quantitative estimation of <i>T</i>_C within first-principles density functional theory shows that even a moderate lattice expansion allows superconductivity in Ta to persist down to sizes much lower than the conventional Anderson limit, which can be traced to anomalous softening of a phonon due to its coupling with electrons. This appears to indicate the possibility of bypassing the Anderson criterion by suitable crystal engineering and obtaining superconductivity at arbitrarily small sizes, an obviously exciting prospect for futuristic quantum technologies. We take a critical look at how the lattice expansion modifies the Anderson limit, an issue of fundamental interest to the study of nanoscale superconductivity

Machine Learning and Statistical Analysis for Materials Science: Stability and Transferability of Fingerprint Descriptors and Chemical Insights

In the paradigm of virtual high-throughput screening for materials, we have developed a semiautomated workflow or “recipe” that can help a material scientist to start from a raw data set of materials with their properties and descriptors, build predictive models, and draw insights into the governing mechanism. We demonstrate our recipe, which employs machine learning tools and statistical analysis, through application to a case study leading to identification of descriptors relevant to catalysts for CO₂ electroreduction, starting from a published database of 298 catalyst alloys. At the heart of our methodology lies the Bootstrapped Projected Gradient Descent (BoPGD) algorithm, which has significant advantages over commonly used machine learning (ML) and statistical analysis (SA) tools such as the regression coefficient shrinkage-based method (LASSO) or artificial neural networks: (a) it selects descriptors with greater stability and transferability, with a goal to understand the chemical mechanism rather than fitting data, and (b) while being effective for smaller data sets such as in the test case, it employs clustering of descriptors to scale far more efficiently to large size of descriptor sets in terms of computational speed. In addition to identifying the descriptors that parametrize the <i>d</i>-band model of catalysts for CO₂ reduction, we predict work function to be an essential and relevant descriptor. Based on this result, we propose a modification of the <i>d</i>-band model that includes the chemical effect of work function, and show that the resulting predictive model gives the binding energy of CO to catalyst fairly accurately. Since our scheme is general and particularly efficient in reducing a set of large number of descriptors to a minimal one, we expect it to be a versatile tool in obtaining chemical insights into complex phenomena and development of predictive models for design of materials