Why choosing the right partner is important: stabilization of ternary CsyGUAxFA(1-y-x)PbI3 perovskites

Entropy-Driven Stabilization of Multielement Halide Double-Perovskite Alloys

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 m₀) 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.

Investigation of Opto-Electronic Properties and Stability of Mixed-Cation Mixed-Halide Perovskite Materials with Machine-Learning Implementation

The feasibility of mixed-cation mixed-halogen perovskites of formula AxA’1−xPbXyX’zX”3−y−z is analyzed from the perspective of structural stability, opto-electronic properties and possible degradation mechanisms. Using density functional theory (DFT) calculations aided by machine-learning (ML) methods, the structurally stable compositions are further evaluated for the highest absorption and optimal stability. Here, the role of the halogen mixtures is demonstrated in tuning the contrasting trends of optical absorption and stability. Similarly, binary organic cation mixtures are found to significantly influence the degradation, while they have a lesser, but still visible effect on the opto-electronic properties. The combined framework of high-throughput calculations and ML techniques such as the linear regression methods, random forests and artificial neural networks offers the necessary grounds for an efficient exploration of multi-dimensional compositional spaces.