Effects of Changing Atomic Concentration of a Single Element on the Yield Strength of High-Entropy Alloys: A Causal Inference Study: Effects of Changing Atomic Concentration of a Single Element on the Yield Strength of High-Entropy Alloys: A Causal Inference Study: N. H. Chau, T. Yamamoto
Saved in:
| Title: | Effects of Changing Atomic Concentration of a Single Element on the Yield Strength of High-Entropy Alloys: A Causal Inference Study: Effects of Changing Atomic Concentration of a Single Element on the Yield Strength of High-Entropy Alloys: A Causal Inference Study: N. H. Chau, T. Yamamoto |
|---|---|
| Authors: | Chau, Nguyen Hai1 (AUTHOR) chaunh@vnu.edu.vn, Yamamoto, Tomoyuki2 (AUTHOR) tymmt@waseda.jp |
| Source: | Journal of Electronic Materials. May2025, Vol. 54 Issue 5, p3663-3674. 12p. |
| Subjects: | High-entropy alloys, Conduction electrons, Valence fluctuations, Causal inference, Support vector machines |
| Abstract: | This study investigates the causal effects of changing the atomic concentration of a single element within high-entropy alloys (HEAs) on their yield strength. We propose a causal inference model for HEAs, named HEACM, and estimate the effects using the double/debiased machine learning framework. Four machine learning methods, including the generalized linear model (GLM), random forests (RF), support vector machine (SVM), and extreme gradient boosting (XGB), are used as estimators within HEACM. We assess the effects of changes in valence electron concentration, mixing enthalpy, and mixing entropy on yield strength resulting from these atomic concentration changes. Experimental results indicate that a positive unit change in valence electron concentration and mixing enthalpy decreases yield strength by averages of 252.3 MPa and 29.8 MPa, respectively, while a positive unit change in mixing entropy results in an average increase in yield strength of 217.4 MPa. The XGB method provides the smallest 95% confidence interval size, suggesting it is the most reliable estimator. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Electronic Materials is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | This study investigates the causal effects of changing the atomic concentration of a single element within high-entropy alloys (HEAs) on their yield strength. We propose a causal inference model for HEAs, named HEACM, and estimate the effects using the double/debiased machine learning framework. Four machine learning methods, including the generalized linear model (GLM), random forests (RF), support vector machine (SVM), and extreme gradient boosting (XGB), are used as estimators within HEACM. We assess the effects of changes in valence electron concentration, mixing enthalpy, and mixing entropy on yield strength resulting from these atomic concentration changes. Experimental results indicate that a positive unit change in valence electron concentration and mixing enthalpy decreases yield strength by averages of 252.3 MPa and 29.8 MPa, respectively, while a positive unit change in mixing entropy results in an average increase in yield strength of 217.4 MPa. The XGB method provides the smallest 95% confidence interval size, suggesting it is the most reliable estimator. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 03615235 |
| DOI: | 10.1007/s11664-025-11852-4 |
