| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Magnesium Technology 2023
|
| Presentation Title |
A Theoretical Model for Predicting Stacking Fault Energies of Ternary Magnesium Alloys Based on High-throughput Calculation and Machine Learning |
| Author(s) |
Qiwen Qiu, Jun Song |
| On-Site Speaker (Planned) |
Qiwen Qiu |
| Abstract Scope |
Magnesium (Mg) and its alloys are the lightest structural metals with a high specific strength. Yet they suffer from low ductility, which limits their wide industrial applications. The stacking fault energy (SFE) is an important property for understanding the plastic behaviors of Mg. Although the SFEs of Mg alloys have been widely studied, general quantitative models to accurately predict SFEs in Mg alloys are still absent. Moreover, the SFE of common ternary alloys is rarely studied. We carry out high-throughput calculations to show the effects of single solutes and solute pairs on SFEs in ternary Mg systems. With the help of machine learning, a theoretical model for predicting SFE has been developed. The work provides some fundamental mechanistic insights for understanding dislocation behaviors in Mg alloys and useful ICME tools in developing rational alloy design recipes for Mg alloys with enhanced ductility. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Mechanical Properties, ICME, Computational Materials Science & Engineering |