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

2025-08-06

AI Summary

High-entropy alloys (HEAs) suffer from scarce, heterogeneous experimental data and a lack of integrated chemical–processing–microstructural–property labels, hindering reliable modeling of composition–property relationships. Method: This study proposes a data-driven framework for face-centered cubic (FCC) HEAs that maps compositional inputs to six mechanical properties—including yield strength and ultimate tensile strength—via element-wise sensitivity analysis, an encoder–decoder neural network, and multi-objective Bayesian hyperparameter optimization. Contribution/Results: It is the first systematic investigation revealing the nonlinear regulatory mechanisms by which key elements govern nanoscale indentation brittleness response and high strength ratios. The proposed model significantly outperforms conventional regression methods in predicting yield strength and strength ratios (R² improvement ≥ 0.18), enabling robust inverse compositional design of FCC HEAs under small-data regimes.

Technology Category

• Search and Optimization: Heuristic Search
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Hardware-aware ML

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Predicting mechanical properties of FCC High Entropy Alloys
• Overcoming data scarcity in chemistry-process-structure-property relationships
• Mapping alloy composition to mechanical behavior using advanced models

Innovation

• Bayesian multi-objective hyperparameter optimization
• Encoder-decoder based chemistry property models
• Sensitivity analyses for elemental contributions