Multimodal deep-learning optimization of chiroptical properties in all-inorganic perovskite-coated TiO2 nanohelices and inverse-design transfer to organic chiral luminophores

Abstract

Abstract Circularly polarized luminescence (CPL) has been catching increasing attention for developing advanced photonic displays, quantum communication, bioimaging, and chiral sensing. All-inorganic chiral luminophores are superior to their organic or organic-inorganic hybrid counterparts in thermal stability, environmental robustness and device compatibility, but limited by the difficulty in fabrication and low luminescence dissymmetry factor ( g lum  < 0.1), whereby g lum is generally applied to evaluate the purity of circular polarization of CPL. Herein, chiral TiO 2 nanohelices (NHs) act as chiral templates that are conformally coated with achiral perovskite luminophores composed of cesium lead bromides, to form all-inorganic chiral core@shell nano-luminophores. Chirality transmission from TiO 2 NHs to perovskites accounts for the generation of CPL. Given by the complex and multifactorial experimental conditions, the manual engineering of fabrication procedure leads to an optimized g lum  = 0.2. To further optimize g lum , we develop OptiCPL, a few-shot multimodal deep-learning framework that integrates spectral and morphological features, to boost g lum from 0.20 to 0.35 through model prediction and experimental validation. In addition, the OptiCPL model is transferrable to polymer F8BT-based chiral organic luminophores, achieving g lum  = 0.87. This work establishes a synergistic chiral core@shell approach and offers a transferable deep-learning framework for designing high- g lum CPL materials.