Hard machine learning can predict hard materials


Superhard materials are in high demand by industry, for use in applications ranging from energy production to aerospace, but finding suitable new materials has largely been a matter of trial and error, based on classical hard materials such as diamonds. In a paper in Advanced Materials, researchers from the University of Houston (UH) and Manhattan College report a machine-learning model that can accurately predict the hardness of new materials, allowing scientists to more readily find compounds suitable for use in a variety of applications. Materials that are superhard – defined as those with a hardness value exceeding 40 gigapascals on the Vickers scale, meaning it would take more than 40 gigapascals of pressure to leave an indentation on the material's surface – are rare. "That makes identifying new materials challenging," said Jakoah Brgoch, associate professor of chemistry at UH and corresponding author of the paper. "That is why materials like synthetic diamond are still used even though they are challenging and expensive to make."

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