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

TL;DR

This study uses advanced modeling and sensitivity analysis to uncover how elemental composition influences mechanical properties in FCC high entropy alloys, aiding predictive design in materials science.

Contribution

Introduces encoder-decoder models optimized via Bayesian methods to predict mechanical properties from alloy compositions, revealing key compositional influences.

Findings

Models outperform traditional regressors in predicting yield strength and UTS/YS ratio.

Sensitivity analyses identify critical elements affecting brittle and fractured responses.

Insights into compositional factors linked to mechanical behavior in FCC high entropy alloys.

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.