AI Learns How to Approximate Light Scattering to Speed Inverse Design

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A computational neural network (a form of artificial intelligence) was able to learn how a multilayered nanoparticle scatters light by learning the relationship between the nanoparticle’s structure and its behavior, based on thousands of training examples. After the relationship was learned, the program could essentially be run backward to design a particle with the desired set of light-scattering properties.

This inverse design technique could lead to a way to custom-design nanoparticles for use in displays, cloaking systems, or biomedical devices. It could also provide a means to predict the physical properties of nanoengineered materials without requiring intensive, time-consuming computation.

An example of nanoparticles that reflect a particular color of light based on size and composition. MIT, the Wikipedia.
This cloaking grenade, used for hiding troop operations from view on the battlefield, is an example of nanoparticles that reflect a particular color of light based on their exact size and composition. Work by MIT researchers provides a way to predict the light-scattering properties of layered nanoparticles — or to design particles to match a desired type of light-scattering behavior. Courtesy of Wikipedia.

Researchers from Massachusetts Institute of Technology (MIT) tested their neural network on a nanophotonics system composed of layers of nanoparticles. Each layer was made of a different material and was of a different thickness. The sizes of the nanoparticles were comparable to, or smaller than, the wavelengths of visible light.

The team found that the particles scattered light differently, depending on the details of the layers and the wavelength of the incoming beam. Rather than computationally calculating these effects for multilayered nanoparticles, researchers decided to see if the neural network could predict the way a particle would scatter by determining an underlying pattern that would allow it to extrapolate.

“The simulations are very exact. . . . But they are numerically quite intensive, so it takes quite some time. What we want to see here is, if we show a bunch of examples of these particles, many, many different particles, to a neural network, whether the neural network can develop ‘intuition’ for it,” said researcher John Peurifoy.

The neural network was able to predict reasonably well the exact pattern of a graph of light scattering versus wavelength, and in much less time than the computer simulations. Researchers found that the network needed to be trained on only a small sampling of the data to approximate the simulation to high precision.

The team’s next step was to run the program in reverse, using a set of desired scattering properties as a starting point, to see if the neural network could work out the exact combination of nanoparticle layers needed to achieve the desired output. Researchers found that when the trained neural network was run backward, it performed well, compared to other standard inverse design methods.

“It will actually do it much quicker than a traditional inverse design,” said professor Marin Soljacic.

Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Further, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical.

“It’s difficult to have apples-to-apples exact comparisons, but you can effectively say that you have gains on the order of hundreds of times. So the gain is very substantial — in some cases, it goes from days down to minutes,” said Peurifoy.

The research was published in Science Advances (doi:10.1126/sciadv.aar4206).

Published: June 2018
Nanopositioning refers to the precise and controlled movement or manipulation of objects or components at the nanometer scale. This technology enables the positioning of objects with extremely high accuracy and resolution, typically in the range of nanometers or even sub-nanometer levels. Nanopositioning systems are employed in various scientific, industrial, and research applications where ultra-precise positioning is required. Key features and aspects of nanopositioning include: Small...
An SI prefix meaning one billionth (10-9). Nano can also be used to indicate the study of atoms, molecules and other structures and particles on the nanometer scale. Nano-optics (also referred to as nanophotonics), for example, is the study of how light and light-matter interactions behave on the nanometer scale. See nanophotonics.
Nanophotonics is a branch of science and technology that explores the behavior of light on the nanometer scale, typically at dimensions smaller than the wavelength of light. It involves the study and manipulation of light using nanoscale structures and materials, often at dimensions comparable to or smaller than the wavelength of the light being manipulated. Aspects and applications of nanophotonics include: Nanoscale optical components: Nanophotonics involves the design and fabrication of...
artificial intelligence
The ability of a machine to perform certain complex functions normally associated with human intelligence, such as judgment, pattern recognition, understanding, learning, planning, and problem solving.
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