| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Magnesium Technology 2021
|
| Presentation Title |
Mechanisms and Machine Learning for Magnesium Alloys Design |
| Author(s) |
Zongrui Pei |
| On-Site Speaker (Planned) |
Zongrui Pei |
| Abstract Scope |
We will show our extensive high-throughput studies for Mg alloys through both the dislocation and twinning mechanisms. Possible descriptors for the mechanisms are explored and a united picture is demonstrated, which is consistent with available experiments. There are two major contributions of this work, i.e., (i) The relationship between two well-acknowledged deformation mechanisms based on dislocations is clarified; (ii) Machine learning models show that it is possible to design ductile Mg alloys without the prior knowledge for deformation mechanisms. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Magnesium, Machine Learning, Computational Materials Science & Engineering |