Transfer learning-aided, artificial intelligence approach to evaluate optical properties of materials for…

Abstract

In this study, an artificial intelligence-assisted evaluation of the refractive indices of different materials is performed. The evaluation of the refractive index is necessary to design photonic devices for various classical and quantum photonic applications, and to theoretically analyse experimental results. The refractive index is one of the crucial parameters that affect the interaction of light with matter. Although the refractive index can be measured experimentally, its estimation via computational means provide the advantage of not requiring costly instrumentation, trained researchers and could avoid potential laboratory-based uncertainties. Therefore, in this study, artificial intelligence is implemented to estimate the optical constants of two types of materials, i.e., noble metals, and semiconductors. To this end, an artificial neural network is used to estimate the real and the imaginary parts of the refractive index of a specific material for various wavelengths of light. Then, the knowledge of the trained neural network is employed to initiate and train new neural networks to estimate the refractive indices of other materials. For the training of a neural network, various methods can be deployed. However, the application of the training can be a time-consuming process. A transfer learning approach is here implemented to provide a faster training solution for the problem under study. The implementation of transfer learning allows training the neural networks for a new material with higher speed or accuracy when compared to non-knowledge transfer-based training. Our results show the efficiency of the proposed technique in accurately evaluating the real and imaginary parts of the refractive index of different materials.

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BibTeX RIS

BibTeX

@misc{habibi2026transfer,
  title = {{Transfer learning-aided, artificial intelligence approach to evaluate optical properties of materials for classical and quantum photonic applications}},
  author = {Mohammad Reza Habibi and Luca Sapienza and Juan C. Vasquez},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.15904},
  eprint = {2610.15904},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.15904},
}

RIS

TY  - UNPB
TI  - Transfer learning-aided, artificial intelligence approach to evaluate optical properties of materials for classical and quantum photonic applications
AU  - Mohammad Reza Habibi
AU  - Luca Sapienza
AU  - Juan C. Vasquez
PY  - 2026
DA  - 2026/10/11
PB  - arXiv
DO  - 10.48550/arxiv.2610.15904
UR  - https://arxiv.org/abs/2610.15904
AB  - In this study, an artificial intelligence-assisted evaluation of the refractive indices of different materials is performed. The evaluation of the refractive index is necessary to design photonic devices for various classical and quantum photonic applications, and to theoretically analyse experimental results. The refractive index is one of the crucial parameters that affect the interaction of light with matter. Although the refractive index can be measured experimentally, its estimation via computational means provide the advantage of not requiring costly instrumentation, trained researchers and could avoid potential laboratory-based uncertainties. Therefore, in this study, artificial intelligence is implemented to estimate the optical constants of two types of materials, i.e., noble metals, and semiconductors. To this end, an artificial neural network is used to estimate the real and the imaginary parts of the refractive index of a specific material for various wavelengths of light. Then, the knowledge of the trained neural network is employed to initiate and train new neural networks to estimate the refractive indices of other materials. For the training of a neural network, various methods can be deployed. However, the application of the training can be a time-consuming process. A transfer learning approach is here implemented to provide a faster training solution for the problem under study. The implementation of transfer learning allows training the neural networks for a new material with higher speed or accuracy when compared to non-knowledge transfer-based training. Our results show the efficiency of the proposed technique in accurately evaluating the real and imaginary parts of the refractive index of different materials.
ER  -

Abstract

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