Once these models could predict properties accurately, the team used algorithms to improve the results. These algorithms generated many possible metal combinations, kept the best ones, and mixed them to create even better versions. Over time, the system was able to zoom in on the most promising alloy designs.
“Our design workflow, combining advanced machine learning and evolutionary algorithms, provides interpretable insights into materials’ structure-property relationships, offering a robust approach for the discovery of diverse advanced materials,” Fangxi Wang, first author and a postdoc researcher at Virginia Tech, said.