|About this Abstract
||3rd World Congress on High Entropy Alloys (HEA 2023)
||Unraveling the mechanisms of stability in CoxMo70-xFe10Ni10Cu10 high entropy alloys via physically interpretable graph neural networks
||James Chapman, Miguel Moreno Tenorio
|On-Site Speaker (Planned)
In recent years high entropy alloys (HEA) have become a topic of significant interest due to their combinatorial nature, showing promise for hypersonics and catalysts. In particular, the HEA system CoxMo70-xFe10Ni10Cu10 has been studied experimentally and computationally due to its reported superiority as a catalyst for ammonia decomposition. However, such catalytic reactions take place at elevated temperatures, leading to potential HEA instability and eventual phase separation at catalytically active temperatures. To this end, we combine density functional theory (DFT) calculations of mixing free energies, that include mixing and vibrational entropy terms, with physics-inspired graph neural networks (GNN) and consider binary (A ↔ B + C), ternary and quaternary decomposition routes. We show that by learning the mixing free energy with our GNN framework we can rank geometric and chemical HEA features to better understand which features are more important than others at stabilizing HEA stability at catalytically active temperatures.
||Planned: Metallurgical and Materials Transactions