Synthesis of Machine Learning-Predicted Cs₂PbSnI₆ Double Perovskite Nanocrystals.

Abstract

Halide perovskites are positioned at the forefront of photonics, optoelectronics, and photovoltaics, owing to their excellent optical properties, with emission wavelengths ranging from blue to near-infrared, and their ease in manufacturing. However, their vast composition space and the corresponding emission energies are still not fully mapped, and guided high-throughput screening that allows for targeted material synthesis would be desirable. To this end, we use experimental data from the literature to build a machine learning model, predicting the band gap of 10,920 possible compositions. Focusing on one of the most promising candidates, Cs₂PbSnI₆, we validate the model by synthesizing and characterizing nanocrystals of the ordered 2-2 elpasolite (double perovskite) structure. The measured photoluminescence spectra agree with both ab initio GW band structure calculations and the machine learning-predicted band gap. Therefore, our study not only provides a machine learning model for the composition space of the halide perovskites but also introduces elpasolite Cs₂PbSnI₆ as a promising candidate material for optoelectronic applications.