Researchers explore use of machine learning to predict bulk metallic glass formation

Researchers from the Yale School of Engineering and Applied Science are analyzing the effectiveness of a machine learning tool in predicting bulk metallic glass formability. A Yale-led study has taken this hurdle on, exploring the use of a machine learning model for the prediction of bulk metallic glass formation. “This work begins to address that question so that new machine learning methods can be developed for bulk metallic glass design” O’Hern said. “It reveals a very powerful idea: complex material science problems like bulk metallic glass formation require physical insights to develop effective and predictable machine learning models,” Liu said. Since a large amount of work has focused on comparing different machine learning tools in the past, the team’s approach permitted them to compare a machine learning approach with traditional computer-aided human learning, providing a valuable insight into the applications of machine learning in material design.