Towards an understanding of photoluminescence in lead-free Cs2AgxNa1−xBiyIn1−yCl6 double perovskites by machine learning prediction from density functional theory ground state properties

Abstract

Halide double perovskites are an emerging class of lead-free materials for optoelectronic and photovoltaic applications. Their properties can be tuned by changing ion ratios on different sublattices. An example is Cs₂Ag_xNa_1−xBi_yIn_1−yCl₆ (CANBIC), which shows impressive photoluminescence for a small range of compositions. In this work, we combine perfect experimental composition control in high-throughput synthesis with density functional theory (DFT) calculations and machine learning to identify the subspace of optimal ion ratios. For the example of CANBIC, we demonstrate that important excited state parameters determining photoluminescence can be successfully predicted by using only high-throughput DFT ground state data in a two-step machine learning algorithm. This approach reveals the relevant ground state features for the observed photoluminescence and is in accordance with the self-trapped exciton mechanism.