Harnessing artificial intelligence for the design and discovery of next-generation optical materials

This paper explores the integration of artificial intelligence (AI) and machine learning (ML) techniques into the design and discovery of next-generation optical materials. Optical materials such as 0D chalcogenides, sulfides, and nano-structured compounds are critical to the advancement of photonic applications but remain challenging to engineer due to the vastness of chemical design space and the complexity of material behaviors. The study outlines the evolution of optical materials, including their historical development, physical classification, and the persistent challenges in meeting modern demands—especially in the UV and IR spectrums. Emphasis is placed on AI’s role in accelerating discovery via inverse design, data-driven workflows, and predictive modeling. Case studies demonstrate how AI has been used to discover materials with specific optical properties, such as refractive index and chromatic dispersion. Additionally, ethical considerations and interdisciplinary barriers in the adoption of AI are discussed. The paper concludes by emphasizing the transformative potential of self-driving laboratories and AI-enabled modeling in achieving high-throughput synthesis, optimization, and real-time experimentation, laying the groundwork for a paradigm shift in materials science.

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