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.

Keywords: band gap; crystallography; elpasolite; machine learning; nanocrystals; perovskite.