Neural Networks Search for New Materials

Mentioned previously here.   Novel use of 'creativity' to search among possible solutions.

Neural networks facilitate optimization in the search for new materials
by David L. Chandler, Massachusetts Institute of Technology

An iterative, multi-step process for training a neural network, as depicted at top left, leads to an assessment of the tradeoffs between two competing qualities, as depicted in graph at center. The blue line represents a so-called Pareto front, defining the cases beyond which the materials selection cannot be further improved. This makes it possible to identify specific categories of promising new materials, such as the one depicted by the molecular diagram at right.  

When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered, and multiple criteria that need to be met and optimized at once. Now, researchers at MIT have found a way to dramatically streamline the discovery process, using a machine learning system.

As a demonstration, the team arrived at a set of the eight most promising materials, out of nearly 3 million candidates, for an energy storage system called a flow battery. This culling process would have taken 50 years by conventional analytical methods, they say, but they accomplished it in five weeks.

The findings are reported in the journal ACS Central Science, in a paper by MIT professor of chemical engineering Heather Kulik, Jon Paul Janet Ph.D. '19, Sahasrajit Ramesh, and graduate student Chenru Duan. ... "