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

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 (glum < 0.1), whereby glum is generally applied to evaluate the purity of circular polarization of CPL. Herein, chiral TiO2 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 TiO2 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 glum = 0.2. To further optimize glum, we develop OptiCPL, a few-shot multimodal deep-learning framework that integrates spectral and morphological features, to boost glum 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 glum = 0.87. This work establishes a synergistic chiral core@shell approach and offers a transferable deep-learning framework for designing high-glum CPL materials.