Artificial Intelligence-Enabled Chiral Functional Materials Design
Chiral functional materials, characterized by intrinsic spatial asymmetry, hold transformative potential in photonics, enantioselective synthesis, quantum technologies, and biomedicine. However, their rational design and discovery are impeded by the vast chemical space and complex structure-property relationships, rendering traditional trial-and-error approaches inefficient and costly. This review critically examines the paradigm-shifting role of artificial intelligence (AI) in accelerating the discovery and optimization of chiral functional materials. We highlight recent AI-driven breakthroughs, emphasizing machine learning (ML) algorithms that excel in identifying patterns within high-dimensional data, thereby enabling rapid virtual screening and elucidation of intricate structure-property correlations. Key applications span from predicting enantioselectivity in asymmetric catalysis to designing circularly polarized luminescent materials and chiral metamaterials. Notably, ML models have achieved predictive accuracies exceeding 90% in classifying chiral structures and have reduced computational screening times by orders of magnitude. The integration of AI with automated synthesis platforms further enables closed-loop optimization, as demonstrated in the autonomous discovery of optically active chiral perovskite nanocrystals. This review underscores that AI not only accelerates materials discovery but also fosters cross-disciplinary innovation, positioning itself as an indispensable tool for the next generation of chiral functional materials. By synthesizing recent progress, we provide a roadmap for leveraging AI to navigate the complex landscape of chiral materials, ultimately expediting the translation of laboratory innovations into practical applications.