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Scientists Use Machine Learning to Accelerate Materials Discovery

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metastable phase diagrams for carbon

The final product of the machine learning algorithm: metastable phase diagrams for carbon.

Credit: Argonne National Laboratory

Scientists at the U.S. Department of Energy's Argonne National Laboratory have recently demonstrated an automated process for identifying and exploring promising new materials by combining machine learning and high performance computing. The approach could help accelerate the discovery and design of useful materials.

Using the single element carbon as a prototype, the algorithm predicted the ways in which atoms order themselves under a wide range of temperatures and pressures to make up different substances. From there, it constructed a series of what scientists call phase diagrams — a kind of map that helps guide their search for new and useful states of matter. The study is published in Nature Communications.

"We trained a computer to probe, question, and learn how carbon atoms could be organized to create phases that we might not find on earth or that we don't fully understand, thereby automating a whole step in the materials development process," says Pierre Darancet, an Argonne scientist and author on the study. ‚Äč"The more of this process a computer can handle on its own, the more materials science we can get done."

From Argonne National Laboratory
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