An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites

Lead-free double perovskites are regarded as stable and green optoelectronic alternatives to single perovskites, but may exhibit indirect band gaps and high effective masses, thus limiting their maximum photovoltaic efficiency. Considering that the trial-and-error experimental and computational approaches cannot quickly identify ideal candidates, we propose an ensemble learning workflow to screen all suitable double perovskites from the periodic table, with a high predictive accuracy of 92% and a computed speed that is similar to 10(8) faster than ab initio calculations. From similar to 23 314 unexplored double perovskites, we successfully identify six candidates that exhibit suitable band gaps (1.0-2.0 eV), where two have direct band gaps and low effective masses. They all show good thermal stabilities that are hopefully able to be synthesized. The proposed ML workflow immensely shortens the screening cycle for double perovskites, which will greatly promote the development and application of photovoltaic devices.

Automated summary

Lead-free double perovskites are considered stable and environmentally friendly optoelectronic alternatives, but their indirect band gaps and high effective masses may limit their efficiency. The proposed ensemble learning workflow successfully screened out six suitable candidates from over 23,314 unexplored double perovskites, two of which exhibit promising characteristics for application in photovoltaic devices. This machine learning approach greatly shortens the screening process and can significantly promote the development of photovoltaic technology.