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.

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