Navigating the complexity of hybrid materials without structural dependency: PerovGNN as a map

Hybrid organic-inorganic materials, spearheaded by perovskites, have showcased exceptional potential in optical and electrical applications. However, finding suitable compositions with desired properties is challenging due to the vast, high-dimensional search space. Traditional machine learning techniques often fall short in capturing crucial interactions within the composites of hybrid organic-inorganic materials, while others necessitate structural information that is often unavailable prior to material synthesis. To address this, we propose PerovGNN, a structure-agnostic graph representation learning method for property prediction. It transforms the chemical formula of each mixed perovskite into a weighted graph of atoms, with weights representing the relative frequency of interactions between neighboring atoms. By embedding both fractional ratios and atomic features, this graph formulation effectively retains crucial mixture information and learns a superior representation required for a specific targeted property. We applied PerovGNN to experimental datasets for mixed HOIPs bandgap prediction, achieving a lower mean absolute error (MAE) of 0.0483 eV compared to other methods. New perovskites with bandgaps ranging from 1.3 to 2.0 eV were experimentally characterized, yielding a consistent MAE of 0.043 eV. Our work presents a unified model for predicting properties of inorganic, organic, and hybrid crystals, serving as a map for researchers navigating vast design spaces.