AI-based optical materials discovery

Amir Ghasemi; Rui Fang; Dagou A. Zeze; Mehdi Keshavarz-Hedayati

doi:10.1117/12.3017154

18 June 2024 AI-based optical materials discovery

Amir Ghasemi, Rui Fang, Dagou A. Zeze, Mehdi Keshavarz-Hedayati

Proceedings Volume 13017, Machine Learning in Photonics; 130170B (2024) https://doi.org/10.1117/12.3017154
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

The field of metamaterials allows for the creation of materials with extraordinary properties. However, the development of materials with specific, custom properties is regarded as a challenging task. Current materials-by- design methodologies hinge on a trial-and-error approach, employing serendipitous techniques that prove inefficient and impractical. Furthermore, the extensive variety of materials and the myriad ways they can be combined in different ratios contribute to an infinite compositional space. Here, we present a universal machine-learning method that identifies the complex, nonlinear relationship between an amorphous metamaterial’s structural characteristics and on-demand optical properties, all within a matter of milliseconds. As a proof of concept, we have demonstrated two practical applications of the method experimentally by developing a custom metasurface-perfect reflector. This innovative approach empowers users to craft materials of interest without depending on intuitions, prior experiences, or extensive simulation and modelling, potentially paving the way for the accelerated discovery of new materials.

Conference Presentation

Citation Download Citation

Amir Ghasemi, Rui Fang, Dagou A. Zeze, and Mehdi Keshavarz-Hedayati "AI-based optical materials discovery", Proc. SPIE 13017, Machine Learning in Photonics, 130170B (18 June 2024); https://doi.org/10.1117/12.3017154

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