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Synthesis of Core-shell Lanthanide-doped Upconversion Nanocrystals for Cellular Applications

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Published on: November 10, 2017

Inverse design of core-shell particles with discrete material classes using neural networks.

Lina Kuhn¹, Taavi Repän², Carsten Rockstuhl^3,4

¹Institute of Theoretical Solid State Physics, Karlsruhe Institute of Technology, 76131, Karlsruhe, Germany. lina.kuhn@kit.edu.

Scientific Reports

|November 8, 2022

Summary

This study introduces a machine learning framework for designing custom core-shell particles. The approach accelerates the design of optical scatterers, enabling on-demand scattering responses.

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Area of Science:

Materials Science
Computational Physics
Optical Engineering

Background:

Designing particles with specific scattering properties is complex.
Current methods for designing optical scatterers are often time-consuming and inflexible.

Purpose of the Study:

To develop a machine learning-assisted framework for designing multi-layered core-shell particles with on-demand scattering responses.
To create a faster and more flexible approach for inverse design of optical scatterers.

Main Methods:

Utilized artificial neural networks (ANNs) as differentiable surrogate models for gradient-based optimization.
Implemented a two-step optimization process to handle continuous geometric parameters and discrete material choices simultaneously.
Employed a classification network to manage varying numbers of shells, addressing problem non-uniqueness and expanding design space.

Main Results:

Achieved high accuracy in predicting scattering spectra using ANNs.
Demonstrated a method that is 1-2 orders of magnitude faster than conventional approaches for both forward prediction and inverse design.
Successfully designed core-shell particles with tailored scattering responses, considering material constraints and variable shell numbers.

Conclusions:

The proposed machine learning framework offers a significant speedup and enhanced flexibility for designing custom optical scatterers.
This approach is scalable for larger and more complex scatterer designs.
The method facilitates the on-demand design of multi-layered core-shell particles with precise scattering characteristics.