Abstract
The intent of the paper is to use specific principles of Materials Design that were developed and applied in the electronics industry for enabling understanding and design of improved high refractive index materials. By combining first-principle based ab-initio, semiempirical interatomic potential methods, and machine learning approaches in conjunction with experimental data, we identified specific determinants of high refractive index materials, which can be critically applied for informing materials design and accelerating discovery. Specifically, it was demonstrated that chalcogenides and perovskites as bulk materials can exhibit higher refractive indices with appropriate engineering of specific aspects of the materials.
| Original language | English |
|---|---|
| Pages (from-to) | 21-32 |
| Number of pages | 12 |
| Journal | IEEE Nanotechnology Magazine |
| Volume | 18 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Jan 2024 |
| Externally published | Yes |
Keywords
- Refractive index
- accelerated discovery
- atomistic methods
- chalcogenides
- density functional theory
- high refractive index
- hybrid methods
- in silico
- inverse problem
- machine learning
- materials design
- measured properties
- perovskites
- polarization
- quantum methods
- semi-empirical
- structure-property relations
- targeted properties
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