Interpretable inverse design of particle spectral emissivity using machine learning

By Mahmoud Elzouka, Charles Yang, Adrian Albert, Sean D. Lubner, Ravi S. Prasher. 2020

3 citations recorded by the source corpus.

Author(s): Elzouka, Mahmoud; Yang, Charles; Albert, Adrian; Lubner, Sean; Prasher, Ravi S | Abstract: We examine the optical properties of a system of nano and micro particles of varying size, shape, and material (including metals and dielectrics, and sub-wavelength and super-wavelength regimes). Training data is generated by numerically solving Maxwel Equations. We then use a combination of decision tree and random forest models to solve both the forward problem (particle design in, optical properties out) and inverse problem (desired optical properties in, range of particle designs out). We show that on even comparatively sparse datasets these machine learning models solve both the forward and inverse problems with excellent accuracy and 4 to 8 orders of magnitude faster than traditional methods. A single trained model is capable of handling the full diversity of our dataset, producing a variety of different candidate particle designs to solve an inverse problem. The interpretability of our models confirms that dielectric particles emit and absorb electromagnetic radiation volumetrically, while metallic particles interaction with light is dominated by surface modes. This work demonstrates the possibility for approachable and interpretable machine learning models to be used for rapid forward and inverse design of devices that span a broad and diverse parameter space.

Subfields: Biomedical Engineering, Civil and Structural Engineering, Electronic, Optical and Magnetic Materials

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Zhi Chen
2. Cynthia Rudin
3. L. Catherine Brinson
4. Alexander C. Ogren
5. O. Muskens
6. Peter R. Wiecha
7. Chiara Daraio
8. C. Girard

In Civil and Structural Engineering

1. L. Catherine Brinson
2. Alexander C. Ogren
3. Chiara Daraio
4. Zhi Chen
5. Cynthia Rudin

In Electronic, Optical and Magnetic Materials

1. Chiara Daraio
2. Peter R. Wiecha
3. C. Girard
4. O. Muskens
5. A. Arbouet

Citations in the corpus

• How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning (2021, background): “Other work also stresses the importance of interpretability for metamaterial design [22, 8, 37].”
• Deep learning in nano-photonics: inverse design and beyond (2020, background): “recent work proposes interpretable machine learning models like decision trees and random forests to understand the physical mechanisms behind inverse design results [124].”