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Many of the predictions of quantum mechanics and the machine learning scheme have been validated by Ramprasad’s UConn collaborators, chemistry professor Greg Sotzing and electrical engineering professor Yang Cao. Sotzing actually made several of the novel polymers, and Cao tested their properties; they came out just as Ramprasad’s computations had predicted.
“What’s most surprising is the level of accuracy with which we can make predictions of the dielectric constant and band gap of a material using machine learning. These properties are generally computed using quantum mechanical methods such as density functional theory, which are six to eight orders of magnitude slower,” says Ramprasad. The group published a paper on their polymer work in Scientific Reports on Feb. 15; and another paper that utilizes machine learning in a different manner, namely, to discover laws that govern dielectric breakdown of insulators, will be published in a forthcoming issue of Chemistry of Materials.
But even if you don’t have access to those academic journals, you can see the predicted properties of every polymer Ramprasad’s group has evaluated in their online data vault, Khazana, which also provides their machine learning apps to predict polymer properties on the fly. They are also uploading data and the machine learning tools from their Chemistry of Materials work, and from an additional recent article published in Scientific Reports on Jan. 19 on predicting the band gap of perovskites, inorganic compounds used in solar cells, lasers, and light-emitting diodes.
Ramprasad is unusually willing to share his results, but that’s because he’s a theoretical materials scientist; what he wants to know is why materials behave the way they do. What about a polymer makes its dielectric constant just so? Or what makes an insulator withstand enormous electric fields without breaking down? But he also wants this understanding to be put to work to design new useful materials rationally. So he makes the results of his calculations freely available in the hope that someone else might look through them, see one, and go, “Wow. I’m looking for a material with exactly those properties!” and then make it. If it works as predicted, they’re both happy.
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