Global instability index as a crystallographic stability descriptor of halide and chalcogenide perovskites

Yang

et al. 2022

J. Phys. Chem. Lett.

Self Cite

Currently, a major obstacle restricting the commercial application of halide perovskites is their low thermodynamic stability. Herein, inspired by the high-stability high-entropy alloys, we theoretically investigated a variety of multielement double-perovskite alloys. First-principles calculations show that the entropy contribution to Gibbs free energy, which offsets the positive enthalpy contribution by up to 35 meV/f.u., can significantly enhance the material stability of double-perovskite alloys. We found that the electronic properties of bandgaps (1.04–2.21 eV) and carrier effective masses (0.34 to greater than 2 m 0) of the multielement double-perovskite alloys can be tuned over a wide range. Meanwhile, the parity-forbidden condition of optical transitions in the Cs2AgInCl6 perovskite can be broken because of the lower symmetry of the configurational disorder, leading to enhanced transition intensity. This work demonstrates a promising strategy by utilizing the alloy entropic effect to further improve the material stability and optoelectronic performance of halide perovskites.

mentioning

confidence: 99%

Entropy-Driven Stabilization of Multielement Halide Double-Perovskite Alloys

Yang

et al. 2022

J. Phys. Chem. Lett.

Self Cite

“…A GII value >0.2 is typically taken as an indication that there is a problem with the reported structure model, and the equation is shown in Supplementary Note 1. The GII values of BCFZnY and BCFZrY are 0.1400 and 0.1395, respectively. − …”

Section: Resultsmentioning

confidence: 98%

Enhanced Proton Conduction with Low Oxygen Vacancy Concentration and Favorable Hydration for Protonic Ceramic Fuel Cells Cathode

ACS Appl. Mater. Interfaces

Zhou

et al. 2022

Self Cite

Protonic ceramic fuel cells (PCFCs), as an efficient energy storage and conversion device, have great potential to solve the serious problems of energy shortage and environmental pollution. Improving the proton conductivity of the promising cathode materials is an effective solution to promote the widespread application of PCFCs at low temperatures (450–650 °C). Herein, considering the high oxygen reduction reaction (ORR) activity of BaCoO3-based perovskite oxide and beneficial proton uptake capacity of Zn-doping, we construct BaCo0.4Fe0.4Zn0.1Y0.1O3‑δ (BCFZnY) as the PCFCs cathode, and compare it with the classic triple-conducting cathode BaCo0.4Fe0.4Zr0.1Y0.1O3‑δ (BCFZrY). Different from the general strategy of increasing the initial oxygen vacancy concentration of cathode materials, this work unveils that enhancing the hydration of perovskite oxide with low oxygen vacancy concentration is a more effective strategy to accelerate the proton diffusion in the electrode. Therefore, the BCFZnY cathode achieved excellent proton conductivities of 8.05 × 10–3 and 6.38 × 10–3 S cm–1 as obtained by hydrogen permeation measurements and peak power densities of 982 and 320 mW cm–2 in a BaZr0.1Ce0.7Y0.1Yb0.1O3‑δ-based anode-supported fuel cell at 600 and 450 °C, respectively.

“…The global instability index (GII) idea was introduced by Feng et al to explain the morphological stability of halide and chalcogenide perovskites. [ 109 ] Earlier, GII was used only for stability analysis of oxide perovskites as the alternative for tolerance factor ( t ) and octahedral factor ( μ ). [ 110 ] The GII values indicate the strains in the crystal, and under these conditions, a limit on the maximum allowed distortions is imposed.…”

Section: Various Applications Of ML In Pscsmentioning

confidence: 99%

Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives

Bansal,

Mishra,

Dixit

et al. 2023

Energy Tech

Within a short period of time, perovskite solar cells (PSC) have attracted paramount research interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC research is significantly accelerated their holistic understanding of device requisite properties. ML techniques are increasingly employed to discover stable perovskite materials, optimize device architecture and processing, and analyze PSC characterization data. This review provides an in‐depth exploration of ML applications in PSC advancement through an analysis of existing literature. The review commences with an introduction to the ML workflow, detailing each step, followed by concise overviews of perovskite materials and PSC operation. Later sections explore the diverse ways ML contributes to PSC development, which ranges from the optoelectronic property prediction of perovskites, discovery of novel perovskites, PSC device structure optimization, and comprehensive PSC analyses. The challenges impeding PSC commercialization are discussed, along with ML's potential to mitigate them. The review concludes by highlighting current limitations in employing ML for PSC research and suggests potential solutions. It also outlines prospective research directions for ML applications in PSC research, aiming to develop highly efficient and stable PSCs.