MIT physicists have developed a new method that could someday provide a way to customize multilayered nanoparticles with preferred properties, potentially for use in cloaking systems, displays, or biomedical devices. It may also help physicists handle a range of thorny research issues, in ways that could in certain cases be orders of magnitude faster than present approaches.

The innovation employs computational neural networks, a form of artificial intelligence, to “learn” how a nanoparticle’s structure influences its behavior, in this case, the way it scatters various colors of light, based on numerous training examples. Then, having learned the association, the program can fundamentally be run backward to design a particle with a preferred set of light-scattering properties — a process known as inverse design.

The results are being published 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 Soljačić, and five others.

While the method could eventually result in practical applications, Soljačić says, the research is mainly of scientific interest as a way of predicting the physical properties of a range of nano-engineered materials without necessitating the computationally intensive simulation processes that are usually used to handle such issues.

Soljačić says that the objective was to study at neural networks, a field that has witnessed a lot of progress and produced 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?”