Physics-driven discovery and bandgap engineering of hybrid perovskites

TL;DR

This paper introduces a structured Gaussian Process modeling approach for discovering and understanding the nonlinear, non-monotonic concentration dependence of bandgap in hybrid perovskites, enabling efficient material design.

Contribution

The authors develop a novel experimental workflow using custom structured Gaussian Process models to jointly discover physical models and experimental behaviors of bandgap tuning in hybrid perovskites.

Findings

Accelerated discovery of bandgap behavior using sGP models.

Rapid convergence of the c-sGP algorithm with minimal data.

Effective modeling of bandgap dependence in hybrid perovskites.

Abstract

The unique aspect of the hybrid perovskites is their tunability, allowing to engineer the bandgap via substitution. From application viewpoint, this allows creation of the tandem cells between perovskites and silicon, or two or more perovskites, with associated increase of efficiency beyond single-junction Schokley-Queisser limit. However, the concentration dependence of optical bandgap in the hybrid perovskite solid solutions can be non-linear and even non-monotonic, as determined by the band alignments between endmembers, presence of the defect states and Urbach tails, and phase separation. Exploring new compositions brings forth the joint problem of the discovery of the composition with the desired band gap, and establishing the physical model of the band gap concentration dependence. Here we report the development of the experimental workflow based on structured Gaussian Process (sGP) models and custom sGP (c-sGP) that allow the joint discovery of the experimental behavior and the underpinning physical model. This approach is verified with simulated data sets with known ground truth, and was found to accelerate the discovery of experimental behavior and the underlying physical model. The d/c-sGP approach utilizes a few calculated thin film bandgap data points to guide targeted explorations, minimizing the number of thin film preparations. Through iterative exploration, we demonstrate that the c-sGP algorithm that combined 5 bandgap models converges rapidly, revealing a relationship in the bandgap diagram of MA1-xGAxPb(I1-xBrx)3. This approach offers a promising method for efficiently understanding the physical model of band gap concentration dependence in the binary systems, this method can also be extended to ternary or higher dimensional systems.