A machine learning approach for the classification of metallic glasses

Metallic glasses possess an extensive set of mechanical properties along with plastic-like processability [1]. As a result, they are a promising material in many industrial applications [2]. However, the successful synthesis of novel metallic glasses requires trial and error, costing both time and resources. Therefore, we propose a high-throughput approach that combines an extensive set of experimental measurements with advanced machine learning techniques. This allows us to classify metallic glasses and predict the full phase diagrams for a given alloy system. Thus this method provides a means to identify potential glass-formers and opens up the possibility for accelerating and reducing the cost of the design of new metallic glasses. [1] J. Schroers, N. Paton, Amorphous metal alloys form like plastics. Adv. Mater. Processes 164(1), 61-63 (2006) [2] W. L. Johnson, Bulk glass-forming metallic alloys: science and technology. MRS Bull. 24, 42–56 (1999)