About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Materials and Chemistry for Molten Salt Systems
|
| Presentation Title |
Effect of Temperature on Molten Chloride Corrosion of High-Entropy Alloys: A Machine Learning Analysis of High-Throughput Experimental Data |
| Author(s) |
Kevin Gautier, Laure Martinelli, Adrien Couet |
| On-Site Speaker (Planned) |
Kevin Gautier |
| Abstract Scope |
Molten salts are attracting increasing interest for low-carbon energy applications due to their high heat capacity and low viscosity, making them promising candidates as nuclear fuels, coolants, or heat transfer fluids in concentrated solar power systems. However, materials exposed to molten chlorides or fluorides at operating temperatures (500-800°C) undergo severe corrosion, limiting long-term reliability.
Building on the work of Goh et al., who screened ~100 additively manufactured CrFeMnNi high-entropy alloys in molten LiCl-KCl-EuCl3 at 500 °C and who identified the Ni tracer diffusion coefficient as a key corrosion descriptor using a random forest machine learning (RF-ML) model, the present study extends this high-throughput approach. This approach relies on a droplet corrosion test enabling rapid, parallelized alloy screening. Its robustness was further improved, and new experiments at 450 and 600 °C expanded the corrosion database to ~300 alloy-condition combinations. The RF-ML model was subsequently retrained to capture temperature-dependent corrosion trends. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, High-Entropy Alloys, |