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The unique aspect of the hybrid perovskites is their tunability, allowing toengineer the bandgap via substitution. From application viewpoint, this allowscreation of the tandem cells between perovskites and silicon, or two or moreperovskites, with associated increase of efficiency beyond single-junctionSchokley-Queisser limit. However, the concentration dependence of opticalbandgap in the hybrid perovskite solid solutions can be non-linear and evennon-monotonic, as determined by the band alignments between endmembers,presence of the defect states and Urbach tails, and phase separation. Exploringnew compositions brings forth the joint problem of the discovery of thecomposition with the desired band gap, and establishing the physical model ofthe band gap concentration dependence. Here we report the development of theexperimental workflow based on structured Gaussian Process (sGP) models andcustom sGP (c-sGP) that allow the joint discovery of the experimental behaviorand the underpinning physical model. This approach is verified with simulateddata sets with known ground truth, and was found to accelerate the discovery ofexperimental behavior and the underlying physical model. The d/c-sGP approachutilizes a few calculated thin film bandgap data points to guide targetedexplorations, minimizing the number of thin film preparations. Throughiterative exploration, we demonstrate that the c-sGP algorithm that combined 5bandgap models converges rapidly, revealing a relationship in the bandgapdiagram of MA1-xGAxPb(I1-xBrx)3. This approach offers a promising method forefficiently understanding the physical model of band gap concentrationdependence in the binary systems, this method can also be extended to ternaryor higher dimensional systems.

Physics-driven discovery and bandgap engineering of hybrid perovskites