Polyelemental, Multicomponent Perovskite Semiconductor Libraries through Combinatorial Screening

Recently, perovskites with multiple cations, metals, and anions have shown very high efficiencies and stabilities for perovskite solar cells. The novel materials frequently exhibit unexpected and beneficial properties, outperforming simpler counterparts. The trend of increasing material complexity requires a systematic strategy to explore polyelemental "multicomponent engineering." Here, a combinatorial approach is introduced to generate all possible, unique combinations within a set of available components. Thus, with each new component, the combinatorial framework can generate the full theoretical parameter space. Based on reported components, the experimental parameter space can then be identified. The exceptional material versatility of perovskites is suited for high-throughput screening, machine-learning, or data mining, laying the foundation for a "perovskite genome project" that thoroughly catalogues the entire material family for desired properties. This can provide the framework for theoretical simulations toward understanding the fundamental working principles of perovskite materials enabling the "next big thing" after perovskites. Finally, informed by literature, a promising candidate list for future material exploration is presented including novel organic-free, Pb-free, and all-inorganic perovskites. These compounds are primary contenders toward stable, high efficiency, and reproducible materials for rapid industrialization of perovskite solar cells, lasers, light-emitting diodes, photo detectors, or particle detectors.