Insights into the Machine Learning Predictions of the Optical Response of Plasmon@Semiconductor Core-Shell Nanocylinders
Abstract
:1. Introduction
2. Materials and Methods
2.1. FDTD Simulations
2.2. CNN and MLP Architectures and Implementations
3. Results and Discussion
3.1. FDTD Simulation Results
3.2. Training of PPRN and SGN
3.3. Comparison between the Results Obtained by PPRN and SGN, with FDTD Simulations
3.4. Interpreting the PPRN and SGN
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Number of Layers | Core Materials | 1st Layer Materials | 2nd Layer Materials | Core Radii Range (nm) | 1st Layer’s Thickness Range (nm) | 2nd Layer’s Thickness Range (nm) |
---|---|---|---|---|---|---|
1 | Au, Ag | NA | NA | 5–75 | NA | NA |
2 | Au, Ag | Si, Ge, TiO2 | NA | 5–40 | 5–40 | NA |
3 | Au, Ag | Si, Ge, TiO2 | Au, Ag | 5–25 | 5–25 | 5–25 |
Categorical Features | Continuous Features | ||||||||
---|---|---|---|---|---|---|---|---|---|
Core Material (Au) | Core Material (Ag) | Shell1 Material (TiO2) | Shell1 Material (Si) | Shell1 Material (Ge) | Shell2 Material (Au) | Shell2 Material (Ag) | Core Radius | Shell1 Thickness | Shell2 Thickness |
SGN Architectures | Activation for the Hidden Layers | Validation Error |
---|---|---|
{1024,1024,200} (used in this study) | relu | 0.019 |
{1024,1024,200} | tanh | 0.034 |
{1024,1024,500,200} | relu | 0.025 |
{512,200} | relu | 0.055 |
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Vahidzadeh, E.; Shankar, K. Insights into the Machine Learning Predictions of the Optical Response of Plasmon@Semiconductor Core-Shell Nanocylinders. Photochem 2023, 3, 155-170. https://doi.org/10.3390/photochem3010010
Vahidzadeh E, Shankar K. Insights into the Machine Learning Predictions of the Optical Response of Plasmon@Semiconductor Core-Shell Nanocylinders. Photochem. 2023; 3(1):155-170. https://doi.org/10.3390/photochem3010010
Chicago/Turabian StyleVahidzadeh, Ehsan, and Karthik Shankar. 2023. "Insights into the Machine Learning Predictions of the Optical Response of Plasmon@Semiconductor Core-Shell Nanocylinders" Photochem 3, no. 1: 155-170. https://doi.org/10.3390/photochem3010010
APA StyleVahidzadeh, E., & Shankar, K. (2023). Insights into the Machine Learning Predictions of the Optical Response of Plasmon@Semiconductor Core-Shell Nanocylinders. Photochem, 3(1), 155-170. https://doi.org/10.3390/photochem3010010