Discovery of New Plasmonic Metals via High‐Throughput Machine Learning (Advanced Optical Materials 18/2022). Issue 18 (19th September 2022)

• Record Type: Journal Article
• Authors: Shapera, Ethan P.; Schleife, André
• Is Part Of: Advanced optical materials. Volume 10:Issue 18(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-19
• Subjects: high throughput -- machine learning -- plasmonics; Optical materials -- Periodicals; Photonics -- Periodicals; 620.11295
• DOI: 10.1002/adom.202270071
• Languages: English
• ISSNs: 2195-1071

Abstract

Abstract : Ethan P. Shapera and André Schleife have constructed, validated, and applied machine learning models to search an online database for potential new plasmonic materials (see article number 2200158 ). The authors predict three high‐quality‐factor metals: AlCu3 (bottom on the cover, plasma frequency = 9.0 eV), ZnCu (middle, plasma frequency = 9.59 eV), and ZnGa3 (top, plasma frequency = 10.64 eV). Furthermore, ZnCu is found to have no interband optical absorption up to 1.9 eV. [Cover image generated by Xinzhi Hu and Ye Lin.]