A data fusion approach to optimize compositional stability of halide perovskites

Armi Tiihonen, Tonio Buonassisi, John W. Fisher, Shijing Sun

Abstract:

Search for resource-efficient materials in vast compositional spaces
is an outstanding challenge in creating environmentally stable
perovskite semiconductors. We demonstrate a physics-constrained
sequential learning framework to subsequently identify the most
stable alloyed organic-inorganic perovskites. We fuse data from
high-throughput degradation tests and first-principle calculations
of phase thermodynamics into an end-to-end Bayesian optimization
algorithm using probabilistic constraints. By sampling just 1.8% of
the discretized CsxMAyFA 1
x
yPbI 3 (MA, methylammonium; FA, for-
mamidinium) compositional space, perovskites centered at
Cs 0.17 MA 0.03 FA 0.80 PbI 3 show minimal optical change under
increased temperature, moisture, and illumination with >17-fold
stability improvement over MAPbI 3 . The thin films have 3-fold
improved stability compared with state-of-the-art multi-halide
Cs 0.05 (MA 0.17 FA 0.83 ) 0.95 Pb(I 0.83 Br 0.17 ) 3 , translating into enhanced
solar cell stability without compromising conversion efficiency. Syn-
chrotron-based X-ray scattering validates the suppression of chem-
ical decomposition and minority phase formation achieved using
fewer elements and a maximum of 8% MA. We anticipate that this
data fusion approach can be extended to guide materials discovery
for a wide range of multinary systems