| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Functional and Structural Thin Films and Coatings & Honorary Palkowski Session
|
| Presentation Title |
J-3: Machine Learning Based Classification of Optical Materials |
| Author(s) |
Sheldon H Fereira, Nuggehalli M. Ravindra |
| On-Site Speaker (Planned) |
Nuggehalli M. Ravindra |
| Abstract Scope |
Investigations of the optical properties of materials are currently of significant interest as they have many impactful applications in emerging fields such as energy, optics, optoelectronics and photonics. In the last few decades, these properties have enabled the development of critical device technologies such as electroluminescent displays, field effect transistors, sensors and solar cells. The goal of this paper is to present a method of classification of optical materials based on reflectance, transmittance and absorptance using Machine Learning. Machine Learning is a set of algorithms implemented with structured data to complete a certain task. This model aims to demonstrate the use of Machine Learning to classify and output the best efficient optical material of single or multiple combination, as function of wavelength, in the region of Visible light to Near Infrared Region (0.4 microns to 2 microns) based on the properties of reflectance, transmittance and absorptance and cataloging them. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Electronic Materials, Modeling and Simulation, Computational Materials Science & Engineering |