big dataNew materials lead to new innovations. Gorilla Glass is a big selling point for smartphones. Kevlar saves lives and has worked its way into consumer products. Lithium-ion batteries have enabled a host of energy-storage applications, from planes to cars to computers. But there’s a problem.

Actually creating a new, game-changing material is a glacially slow process — especially when compared to the rate at which new products relying on those materials hit the market. It took just under nine years for the Boeing 787 Dreamliner to go from a concept to commercial flight. The development of the iPhone began in 2005; the phone was on store shelves by 2007. In contrast, the creation of new materials moves far more slowly, taking about 20 years for all of the necessary research and development.

In an effort to overcome this innovation bottleneck, the White House two years ago announced the Materials Genome Initiative. The venture aims to halve development time for new materials and slash the monetary investment required. And if the name sounds familiar, it should: in the same way the Human Genome Project set out to map the underlying structure of human genes, the Materials Genome Initiative is an attempt to gain a deeper understanding of how the elements interact to give us a diverse set of materials and materials properties. With that foundation of knowledge, scientists and engineers will hopefully be able to create new materials tuned to the exact properties needed for a particular application — and be able to do it much, much faster.

A huge number of atomic combinations and arrangements may have useful properties. However, most arrangements won’t be useful, or even able to be synthesized. Trying to explore the vast world of potential materials in a lab would be both impractical and just plain impossible. So to map out that enormous number of possible materials, several research groups working on the Materials Genome Initiative are using computers to model known and unknown materials. They mine the resulting data to find areas that deserve a more careful examination.

In the years since its inception, the initiative has brought together several successful ventures. Among them are the Materials Project at MIT and the Harvard Clean Energy Project. These two projects have similar theoretical underpinnings for different end goals. MIT’s Materials Project is focused on inorganic solids, especially those for battery materials, while the Clean Energy Project is examining molecules for solar cell applications. Both are powered by huge databases that are populated with information gleaned from Density Functional Theory (DFT) calculations. DFT uses quantum mechanics to predict many properties of the real, physical substances being modeled.

MIT’s Materials Project started about eight years ago, and was catalyzed by the work of Professor Gerbrand Ceder. As a consultant to several companies, Ceder would screen a large number of materials for particular applications. But working with individual companies left the data siloed and locked up. “People would be able to do really creative things with this if we gave this to the world, and this became Materials Project,” he says. Now, MIT’s dataset consists of over 100,000 known and theoretical materials. To make sense of the data and design new materials, MIT researchers use a combination of human intuition and machine learning designed to understand the laws of chemistry. By Alexander Thompson Read more