MIT Physicists Develop AI-based Method Could Speed Development of Specialized Nanoparticles

Saturday, June 2, 2018 - 13:49

MIT physicists have developed a new technique which could someday provide a way to custom-design multilayered nanoparticles with desired properties, potentially for use in displays, cloaking systems, or biomedical devices.

The innovation uses computational neural networks, a form of artificial intelligence, to "learn" how a nanoparticle's structure affects its behavior, in this case the way it scatters different colors of light, based on thousands of training examples, Phys.org reports.

Then, having learned the relationship, the program can essentially be run backward to design a particle with a desired set of light-scattering properties—a process called inverse design.

The findings are being reported in the journal Science Advances, in a paper by MIT senior John Peurifoy, research affiliate Yichen Shen, graduate student Li Jing, professor of physics Marin Soljacic, and five others.

While the approach could ultimately lead to practical applications, Soljacic says, the work is primarily of scientific interest as a way of predicting the physical properties of a variety of nanoengineered materials without requiring the computationally intensive simulation processes that are typically used to tackle such problems.

Soljacic says that the goal was to look at neural networks, a field that has seen a lot of progress and generated excitement in recent years, to see "whether we can use some of those techniques in order to help us in our physics research. So basically, are computers 'intelligent' enough so that they can do some more intelligent tasks in helping us understand and work with some physical systems?"

To test the idea, they used a relatively simple physical system, Shen explains. "In order to understand which techniques are suitable and to understand the limits and how to best use them, we [used the neural network] on one particular system for nanophotonics, a system of spherically concentric nanoparticles." The nanoparticles are layered like an onion, but each layer is made of a different material and has a different thickness.

The nanoparticles have sizes comparable to the wavelengths of visible light or smaller, and the way light of different colors scatters off of these particles depends on the details of these layers and on the wavelength of the incoming beam. Calculating all these effects for nanoparticles with many layers can be an intensive computational task for many-layered nanoparticles, and the complexity gets worse as the number of layers grows.

The speedup in certain kinds of inverse design simulations can be quite significant. Peurifoy says "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 very substantial—in some cases it goes from days down to minutes."

More information: J. Peurifoy el al., "Nanophotonic particle simulation and inverse design using artificial neural networks," Science Advances (2018). advances.sciencemag.org/content/4/6/eaar4206

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