  Expanding the pool of stable halide perovskites with attractive
optoelectronic properties is crucial to addressing current limitations in their
performance as photovoltaic (PV) absorbers. In this article, we demonstrate how
a high-throughput density functional theory (DFT) dataset of halide perovskite
alloys can be used to train accurate surrogate models for property prediction
and subsequently perform inverse design using genetic algorithm (GA). Our
dataset consists of decomposition energies, band gaps, and photovoltaic
efficiencies of nearly 800 pure and mixed composition ABX$_3$ compounds from
both the GGA-PBE and HSE06 functionals, and are combined with ~ 100
experimental data points collected from the literature. Multi-fidelity random
forest regression models are trained on the DFT + experimental dataset for each
property using descriptors that one-hot encode composition, phase, and
fidelity, and additionally include well-known elemental or molecular properties
of species at the A, B, and X sites. Rigorously optimized models are deployed
for experiment-level prediction over > 150,000 hypothetical compounds, leading
to thousands of promising materials with low decomposition energy, band gap
between 1 and 2 eV, and efficiency > 15%. Surrogate models are further combined
with GA using an objective function to maintain chemical feasibility, minimize
decomposition energy, maximize PV efficiency, and keep band gap between 1 and 2
eV; hundreds more optimal compositions and phases are thus discovered. We
present an analysis of the screened and inverse-designed materials, visualize
ternary phase diagrams generated for many systems of interest using ML
predictions, and suggest strategies for further improvement and expansion in
the future.
