Making complex structures out of nanoparticles or polymers, be they for photonic computing or solar cells, typically involves a lot of expensive and time-consuming trial and error in the lab. Theorists hope to simplify the process by developing computer models that will generate recipes that always come out right, but so far, the ones that they’ve made have been too complex to realize in the lab. Now, in the hope of making these algorithms useful to chemists, computer scientists at Microsoft have simplified a model that creates recipes for self-assembling materials.
The new Microsoft models, described this week in the Proceedings of the National Academy of Sciences, are intended to speed the design of new self-assembled structures. Using trial and error, materials scientists have employed nanoparticles to make structures on what’s called the mesoscale. These ordered arrangements of nanoscale particles can have remarkable optical, electrical, and other properties but are difficult to create. “Theory there is sorely lacking,” says Mila Boncheva, a senior scientist at Firmenich, in Geneva, who played an important role in early research on this kind of self-assembly at Harvard University. “What people are currently doing in design is mostly trial and error based on common sense.” The theoretical model is aimed at helping materials scientists figure out much more quickly what the right materials and conditions are for self-assembly of a given structure.
“If you have in mind a form or shape, the model will tell you how to get it,” says Henry Cohn, principal researcher at Microsoft Research New England, who led the work with MIT assistant professor of mathematics Abhinav Kumar. The properties of materials structured on the mesoscale are to a great extent determined by how the individual components, be they polymers or nanoparticles, are arranged relative to each other. For example, silver nanoparticles floating in solution reflect light differently depending on how closely they’re packed–a principle that’s being used to design devices for photonic computing.
Whether or not particles will assemble into a given structure is determined by the forces between them. Electrical charges, for example, play a particularly important role in determining whether two particles will attract or repel each other. The Microsoft model generates a map of how strong these forces need to be. That is, given a desired structure, what should the potential energy between each of the particles and its neighbors be? These models are called potential functions.
“It is easy to design potential functions [on a computer], and really, really hard to generate them in reality,” says George Whitesides, a professor of chemistry at Harvard University and pioneer in self-assembly. Generating these forces requires figuring out what modifications to the particles–say, adding more positively charged groups to polymers–will generate the appropriate forces between individual particles and lead to the assembly of the desired structure.
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