| Abstract Scope |
The investigation of the optical properties of materials is critical for advancing technologies across various domains, including energy, optics, optoelectronics, and photonics. Optical properties play a pivotal role in the development of devices such as electroluminescent displays, field-effect transistors, sensors, and solar cells. In this work, we propose a novel approach for classification of optical materials using Machine Learning (ML) techniques. The proposed ML model is designed to categorize and identify the most efficient optical materials, whether as a single layer or multi-layer, and as a function of wavelength, visible to near-infrared spectrum (0.4 to 2 microns). By analyzing the properties of materials, precise selection, and cataloging, materials with specific optical performance criteria can be identified thus reducing the time required for research. This work underscores the potential of Machine Learning in material science, providing a robust and faster framework for the classification and optimization of materials. |