Harnessing the Complex Compositional Space of High-Entropy Alloys

Poon, S. J.; Qi, Jie; Cheung, Andrew M.

doi:10.1007/978-3-030-77641-1_3

High-Entropy Materials: Theory, Experiments, and Applications 2021

DOI: 10.1007/978-3-030-77641-1_3

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Harnessing the Complex Compositional Space of High-Entropy Alloys

S. J. Poon

Jie Qi

Andrew M. Cheung

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Cited by 3 publications

References 172 publications

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Machine Learning-Based Classification, Interpretation, and Prediction of High-Entropy-Alloy Intermetallic Phases

Qi,

Hoyos,

Poon

2023

High Entropy Alloys & Materials

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Machine Learning-Based Classification, Interpretation, and Prediction of High-Entropy-Alloy Intermetallic Phases

Qi,

Hoyos,

Poon

2023

High Entropy Alloys & Materials

View full text Add to dashboard Cite

Comparing Machine Learning Models for Strength and Ductility in High-Entropy Alloys

Ibarra-Hoyos,

Simmons,

Poon

2024

High Entropy Alloys & Materials

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We compare several machine learning (ML) models that predict the yield strength and plasticity of high-entropy alloys (HEAs) for achieving high-accuracy with notably low root mean square errors (RMSE). Our models, developed using a comprehensive database of single-phase body-centered cubic (BCC) HEAs and BCC + B2 HEAs (where B2 is ordered BCC), integrate advanced feature engineering reflecting the current understanding of electronic factors, atomic ordering informed by mixing enthalpy, and the D parameter associated with stacking fault energy in HEAs. This approach enables systematic comparisons of different ML models, providing deep insights into the mechanical properties of BCC and related alloys. By leveraging genetic algorithms for feature selection and meticulous hyperparameter optimization, our ML framework excels in both predictive power and interpretability. The rigorous validation process includes repeated k-fold cross-validation and leave-one-out cross-validation (LOOCV), ensuring robust generalization. Moreover, our use of Shapley Additive Explanations (SHAP) revealed critical predictors such as the testing temperature-to-melting temperature ratio $$\left(\frac{{T}_{test}}{{T}_{melt}}\right)$$ T test T melt , the mixing enthalpy $$\left(\Delta {\text{H}}_{\text{mix}}\right)$$ Δ H mix and the D parameter, which play key roles in determining plasticity and yield strength. Our work represents an advancement in designing high-performance HEAs with optimized strength and ductility, offering a powerful tool for predicting mechanical properties and exploring new candidate alloys with exceptional mechanical behavior.

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Integrated design of aluminum-enriched high-entropy refractory B2 alloys with synergy of high strength and ductility

Qi,

Fan,

Hoyos

et al. 2024

Sci. Adv.

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Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility. These Al-RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.7 gigapascals, fracture strains exceeding 50%, and notable high-temperature strength retention. They also demonstrate a tensile yield strength of 1.0 gigapascals with a ductility of 9%, albeit with B2 ordering. Furthermore, we identify valence electron count domains for alloy ductility and brittleness with the explanation from density functional theory and provide crucial insights into elemental influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles governing the mechanical properties of advanced structural alloys.

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Harnessing the Complex Compositional Space of High-Entropy Alloys

Cited by 3 publications

References 172 publications

Machine Learning-Based Classification, Interpretation, and Prediction of High-Entropy-Alloy Intermetallic Phases

Machine Learning-Based Classification, Interpretation, and Prediction of High-Entropy-Alloy Intermetallic Phases

Comparing Machine Learning Models for Strength and Ductility in High-Entropy Alloys

Integrated design of aluminum-enriched high-entropy refractory B2 alloys with synergy of high strength and ductility

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