Ab Initio Modeling of Thin-Film TiN: Implications for Machine Learning Optical Metasurface Design
We show that special quasi-random cells can be used within density functional theory to predict the optical properties of TiN thin films and that metasurface inverse design via machine learning depends sensitively on these optical properties.
Authors
- Peter R. Stevenson (ORCID: https://orcid.org/0000-0003-3731-1630)
- John G. Jones (ORCID: https://orcid.org/0000-0001-8271-0038)
- E. G. Suter (ORCID: https://orcid.org/0000-0001-5911-7156)
- Nathan Episcopo (ORCID: https://orcid.org/0000-0002-5390-6944)
- Eric S. Harper (ORCID: https://orcid.org/0000-0002-7058-1686)
- Lirong Sun
- Amy Morren
Institutions
- The University of Texas at El Paso (US)
- Wright-Patterson Air Force Base (US)
- Solutions Through Innovative Technologies (United States) (US)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/oic.2025.thb.6
- Primary Topic
- 2D Materials and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00