AI News, AI-based method could speed development of specialized nanoparticles

AI-based method could speed development of specialized nanoparticles

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

While the approach could ultimately lead to practical applications, Soljačić 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.

The researchers wanted to see if the neural network would be able to predict the way a new particle would scatter colors of light — not just by interpolating between known examples, but by actually figuring out some underlying pattern that allows the neural network to extrapolate.

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.” Sure enough, the neural network was able to predict reasonably well the exact pattern of a graph of light scattering versus wavelength — not perfectly, but very close, and in much less time.

But it came with a price, and the price was that we had to first train the neural network, and in order to do that we had to produce a large number of examples.” Once the network is trained, though, any future simulations would get the full benefit of the speedup, so it could be a useful tool for situations requiring repeated simulations.

“But very often in order to set up a given inverse design problem, it takes quite some time, so in many cases you have to be an expert in the field and then spend sometimes even months setting it up in order to solve it.” But with the team’s trained neural network, “we didn't do any special preparation for this.

AI-based method could speed development of specialized nanoparticles

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.

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.

'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 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.

The researchers wanted to see if the neural network would be able to predict the way a new particle would scatter colors of light -- not just by interpolating between known examples, but by actually figuring out some underlying pattern that allows the neural network to extrapolate.

The next step was to essentially run the program in reverse, to use a set of desired scattering properties as the starting point and see if the neural network could then work out the exact combination of nanoparticle layers needed to achieve that output.

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