Adapting Explainable Machine Learning to Study Mechanical Properties of 2D Hybrid Halide Perovskites

Abstract

Abstract2D hybrid organic and inorganic perovskites (HOIPs) are used as capping layers on top of 3D perovskites to enhance their stability while maintaining the desired power conversion efficiency (PCE). Therefore, the 2D HOIP needs to withstand mechanical stresses and deformations, making the stiffness an important observable. However, there is no model for unravelling the relationship between their crystal structures and mechanical properties. In this work, explainable machine learning (ML) models are used to accelerate the in silico prediction of mechanical properties of 2D HOIPs, as indicated by their out‐of‐plane and in‐plane Young's modulus. The ML models can distinguish between stiff and non‐stiff 2D HOIPs, and extract the dominant physical feature influencing their Young's moduli, viz. the metal‐halogen‐metal bond angle. Furthermore, the steric effect index (STEI) of cations is found to be a rough criterion for non‐stiffness. Their optimal ranges are extracted from a probability analysis. Based on the strong correlation between the deformation of octahedra and the Young's modulus, the transferability of the approach from single‐layer to multi‐layer 2D HOIPs is demonstrated. This work represents a step toward unravelling the complex relationship between crystal structure and mechanical properties of 2D HOIPs using ML as a tool.