| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Environmental Degradation of Multiple Principal Component Materials
|
| Presentation Title |
Understanding Corrosion of Multi Principal Element Alloys Using Machine Learning |
| Author(s) |
Nick Birbilis, Marzie Ghorbani |
| On-Site Speaker (Planned) |
Nick Birbilis |
| Abstract Scope |
Whilst multi-principal element alloys (MPEAs) remain a promising class of materials owing to several attractive mechanical properties, their corrosion performance is also unique. The corrosion of MPEAs can vary widely depending on the alloy composition (including wide variations from relatively minor compositional changes). Herein, we explore a data science approach to rationalise the corrosion performance of MPEAs, and couple this to mechanistic corrosion processes. Critical characteristics of MPEA corrosion, including incongruent dissolution and complex surface (passive) film development, are discussed. The data science approach (leveraging machine learning) is also explored in the context of development of MPEAs with aqueous corrosion resistance. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Environmental Effects, High-Entropy Alloys, Machine Learning |