A data fusion approach to optimize compositional stability of halide perovskites

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 Cs(x)MA(y)FA(1-x-y)PbI(3) (MA, methylammonium; FA, formamidinium) 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(I0.83Br0.17)(3), translating into enhanced solar cell stability without compromising conversion efficiency. Synchrotron-based X-ray scattering validates the suppression of chemical 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.