Machine Learning-Guided Elucidation of Spacer-Dependent Crystallization Pathways in Quasi-2D Perovskites: Linear vs. Branched Spacer Cations.

Abstract

Quasi-two-dimensional (2D) metal halide perovskites have emerged as a structurally robust, electronically tunable platform for optoelectronic applications. The precise modulation of the n-phase distribution in quasi-2D metal halide perovskites remains a critical challenge for tailoring the resulting optoelectronic properties. However, molecular descriptors governing spacer-dependent crystallization and n-phase distribution remain elusive. Herein, we present an integrated framework utilizing machine learning (ML) to identify the molecular descriptors of spacer cations that control structural evolution across the 2D-3D perovskite landscape. By combining multiple ML regression algorithms with SHapley Additive exPlanations (SHAP) analysis, we identified the melting point and rotatable bond count of spacer cations as the most influential molecular descriptors. These molecular features are associated with aggregational enthalpy and conformational flexibility, which are indicative of the thermodynamic assembly and kinetic diffusion processes that determine the average layer thickness. The ML-integrated frameworks discussed in this study establish a predictive platform that bridges the gap between molecular descriptors and target optoelectronic properties, further providing a data-driven route toward the rational design of lower-dimensional perovskites.