Data Driven Insights into Composition Property Relationships in FCC High Entropy Alloys

• URL: http://arxiv.org/abs/2508.04841v1
• Date: Wed, 06 Aug 2025 19:41:15 GMT
• Title: Data Driven Insights into Composition Property Relationships in FCC High Entropy Alloys
• Authors: Nicolas Flores, Daniel Salas Mula, Wenle Xu, Sahu Bibhu, Daniel Lewis, Alexandra Eve Salinas, Samantha Mitra, Raj Mahat, Surya R. Kalidindi, Justin Wilkerson, James Paramore, Ankit Srivastiva, George Pharr, Douglas Allaire, Ibrahim Karaman, Brady Butler, Vahid Attari, Raymundo Arroyave,
• Abstract summary: Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors.<n>The scarcity of integrated chemistry, process, structure, and property data presents significant challenges for predictive property modeling.<n>This work presents several sensitivity analyses, highlighting key elemental contributions to mechanical behavior.
• Score: 28.495739557732175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors, including aerospace, automotive, and defense industries. However, the scarcity of integrated chemistry, process, structure, and property data presents significant challenges for predictive property modeling. Given the vast design space of these alloys, uncovering the underlying patterns is essential yet difficult, requiring advanced methods capable of learning from limited and heterogeneous datasets. This work presents several sensitivity analyses, highlighting key elemental contributions to mechanical behavior, including insights into the compositional factors associated with brittle and fractured responses observed during nanoindentation testing in the BIRDSHOT center NiCoFeCrVMnCuAl system dataset. Several encoder decoder based chemistry property models, carefully tuned through Bayesian multi objective hyperparameter optimization, are evaluated for mapping alloy composition to six mechanical properties. The models achieve competitive or superior performance to conventional regressors across all properties, particularly for yield strength and the UTS/YS ratio, demonstrating their effectiveness in capturing complex composition property relationships.

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