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 ∼108 faster
than ab initio calculations. From ∼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.