A new computational approach will improve understanding of different states of carbon and guide the search for materials yet to be discovered.
Materials—we use them, wear them, eat them and create them. Sometimes we invent them by accident, like with Silly Putty. But far more often, making useful materials is a tedious and expensive process of trial and error.
Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have recently demonstrated an automated process for identifying and exploring promising new materials by combining machine learning (ML)—a type of artificial intelligence—and high performance computing. The new approach could help accelerate the discovery and design of useful materials.
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