Screening procedure for structurally and electronically matched contact layers for high-performance solar cells: hybrid perovskites

The rapid progress in performance of solar cells based on hybrid halide perovskites means that devices based on these materials have reached a stage where research interest can now focus on development of robust technology. One of the key questions relating to these (and indeed any) devices is their lifetime and stability which in turn is often influenced by the quality of interfaces and junctions within the device. In this study we present a methodology which allows screening for mechanically stable, electronically suitable interface combinations – applying the technique to screen 175 common semiconductors for viability as electron and hole extracting contacts for CH3NH3PbI3. The screening method can be applied to any semiconductor junction problem and relies on easily obtained experimental or theoretical information – electron affinity, ionisation potential, lattice parameters and crystal structure. From the screening we rank the candidates according to a figure of merit, which accounts for band alignment and chemical/mechanical stability of the interface. Our screening predicts stable interfaces with commonly applied electron extraction layers such as TiO2 and ZnO as well giving insight into the optimal polymorphs, surfaces and morphologies for achieving good quality contacts. Finally we also predict potentially effective new hole and electron extraction layers, namely Cu2O, FeO, SiC, GaN, and ZnTe

Computational materials design of crystalline solids

The modelling of materials properties and processes from first principles is becoming sufficiently accurate as to facilitate the design and testing of new systems in silico. Computational materials science is both valuable and increasingly necessary for developing novel functional materials and composites that meet the requirements of next-generation technology. A range of simulation techniques are being developed and applied to problems related to materials for energy generation, storage and conversion including solar cells, nuclear reactors, batteries, fuel cells, and catalytic systems. Such techniques may combine crystal-structure prediction (global optimisation), data mining (materials informatics) and high-throughput screening with elements of machine learning. We explore the development process associated with computational materials design, from setting the requirements and descriptors to the development and testing of new materials. As a case study, we critically review progress in the fields of thermoelectrics and photovoltaics, including the simulation of lattice thermal conductivity and the search for Pb-free hybrid halide perovskites. Finally, a number of universal chemical-design principles are advanced

Absorbate-Induced Piezochromism in a Porous Molecular Crystal

Atmospherically stable porous frameworks and materials are interesting for heterogeneous solid–gas applications. One motivation is the direct and selective uptake of pollutant/hazardous gases, where the material produces a measurable response in the presence of the analyte. In this report, we present a combined experimental and theoretical rationalization for the piezochromic response of a robust and porous molecular crystal built from an extensively fluorinated trispyrazole. The electronic response of the material is directly determined by analyte uptake, which provokes a subtle lattice contraction and an observable bathochromic shift in the optical absorption onset. Selectivity for fluorinated absorbates is demonstrated, and toluene is also found to crystallize within the pore. Furthermore, we demonstrate the application of electronic structure calculations to predict a physicochemical response, providing the foundations for the design of electronically tunable porous solids with the chemical properties required for development of novel gas-uptake media

Designing porous electronic thin-film devices: band offsets and heteroepitaxy

Increasing numbers of electrically active porous framework materials are being reported, with conductivities that make them attractive for technological applications. As design strategies for efficient carrier transport emerge, the next challenge is to incorporate the materials into a functioning device. In thin-film devices interface effects are of critical importance to overall function. In this article we present a method to identify compatible materials combinations to achieve mechanically robust, electronically optimal pairings. The computational screening is based on a two-step procedure: (i) matching of lattice constants to ensure interfaces with minimal epitaxial strain and therefore maximal mechanical and chemical stability; (ii) matching of absolute electron energies to construct energy-band-alignment diagrams, which can be used to screen for particular electronic applications. We apply the methodology to search for zeolitic imidazolate framework (ZIF) type materials that are compatible with native metal electrodes. The procedure allows us to predict simple routes for electrochemical deposition of ZIFs for application as conductive porous electrodes

Halide Perovskite Heteroepitaxy: Bond Formation and Carrier Confinement at the PbS-CsPbBr3 Interface

Control of the stability, transport, and confinement of charge carriers (electrons and holes) at interfaces is a key requirement to realize robust halide perovskite devices. The PbS–CsPbBr3 interface is atomically matched with low lattice strain, opening the potential for epitaxial growth. We assess the atomic nature of the interface using first-principles density functional theory calculations to identify (1) the thermodynamically stable (100) surface termination of the halide perovskite; (2) the most favorable (100)|(100) contact geometry; (3) the strong interfacial chemical bonding between PbS and CsPbBr3; (4) the type I (straddling) band alignment that enables electron and hole confinement in the lead sulfide layer. The combination of metal halide perovskites and IV–VI semiconductors represents an important platform for probing interfacial chemical processes and realizing new functionality

A deep convolutional neural network for real-time full profile analysis of big powder diffraction data

We present Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems. The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO2-ZrO2/Al2O3 catalytic material system consisting of ca. 20,000 diffraction patterns. It is shown that the network predicts accurate scale factor, lattice parameter and crystallite size maps for all phases, which are comparable to those obtained through full profile analysis using the Rietveld method, also providing a reliable uncertainty measure on the results. The main advantage of PQ-Net is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments