|About this Abstract
||2021 TMS Annual Meeting & Exhibition
||Hume-Rothery Symposium: Accelerated Measurements and Predictions of Thermodynamics and Kinetics for Materials Design and Discovery
||High-throughput Synthesis, Characterization and Prediction of Metallic Glass Formation
||John H. Perepezko, Janine Erickson, Dan Thoma, Carter Francis, Paul Voyles, Benjamin Afflerbach, Dane Morgan
|On-Site Speaker (Planned)
||John H. Perepezko
Materials discovery in metallic glass research is limited by the speed of synthesis and the need for experimental data for any model to predict glass formation. Additive manufacturing (AM) provides cooling rates of 103-104 K/s for bulk metallic glass (BMG) formation. In situ alloying enables rapid synthesis of compositional libraries with larger sample sizes than are provided by combinatorial thin films. As a test of the method, elemental powders were used to synthesize alloys in the known glass-forming system Mg-Cu-Y. With disparate melting temperatures, high reactivity, and dissimilar physical and thermal properties this system presents several challenges for AM. Using a tiered high-throughput characterization system, amorphous material was identified in a region of known BMG formation. In parallel with the AM, machine learning was applied to correlate a large database of critical cooling rates with elemental features to predict a favorable glass forming composition range that was confirmed by experiment.