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This preprint studies how to predict phase structures in quaternary equiatomic medium-entropy alloys using machine learning, drawing on a literature-compiled dataset of 731 alloy samples labeled as FCC (158), BCC (151), or mixed/amorphous-like structures (422). Using seven composition-derived features (including VEC, mixing enthalpy, atomic size difference, electronegativity difference, and density statistics), the authors trained random forest, SVM, and XGBoost models with grid-search hyperparameter tuning and ten-fold cross-validation, finding XGBoost performed best with 0.939 accuracy and higher F1-scores across classes. A major caveat is that the work is a preprint with potentially preliminary data and an imbalanced class distribution, and it depends on literature labels rather than experimental verification. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Medium-entropy alloys (MEAs) are a class of alloys composed of a small number (typically three to four) of principal elements in near-equiatomic ratios. They exhibit a balance between the structural stability of high-entropy alloys (HEAs) and the controllability of conventional alloys, demonstrating excellent mechanical properties, thermal stability, and corrosion resistance. However, the design of MEAs still relies primarily on empirical and trial-and-error methods, making it challenging to accurately predict their phase structures and properties. This study proposes a machine learning-based regression and classification framework to predict the phase structures of quaternary equiatomic MEAs. A dataset of 731 alloy samples was compiled from the literature and categorized into three phase structures: face-centered cubic (FCC, 158 samples), body-centered cubic (BCC, 151 samples), and mixed or amorphous-like structures (none, 422 samples). Seven key features including valence electron concentration (VEC), mixing enthalpy (ΔH mix ), atomic size difference (δ), electronegativity difference (Δχ), and maximum, minimum, and average densities—were selected to establish a mathematical model describing the relationship between alloy composition and structure. Three machine learning algorithms—random forest (RFC), support vector machine (SVC), and extreme gradient boosting (XGBoost)—were employed, with hyperparameter optimization conducted using grid search and ten-fold cross-validation. The results indicate that XGBoost is the best-performing model, achieving an accuracy of 0.939 on the test set, outperforming RFC (0.932) and SVC (0.721). Additionally, XGBoost demonstrated superior F1-scores, with 0.947 for the FCC class, 0.923 for BCC, and 0.943 for none, surpassing the other models. These findings confirm that XGBoost effectively identifies key factors influencing phase stability and serves as a reliable predictive tool for the composition optimization of MEAs.
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Posted on 7 Mar 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174132752.29405034/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.
Smart Alloy Design: Predicting FCC, BCC, and Amorphous Phases
in Medium-Entropy Alloys via Interpretable XGBoost Modeling
Zhen Fan1, Shuai Lian 2, Junyao Wang 1, and Yifan Tian 1
1Changchun University of Science and Technology
2Beijing Institute of Technology
March 07, 2025
Abstract
Medium-entropy alloys (MEAs) are a class of alloys composed of a small number (typically three to four) of principal elements
in near-equiatomic ratios. They exhibit a balance between the structural stability of high-entropy alloys (HEAs) and the
controllability of conventional alloys, demonstrating excellent mechanical properties, thermal stability, and corrosion resistance.
However, the design of MEAs still relies primarily on empirical and trial-and-error methods, making it challenging to accurately
predict their phase structures and properties. This study proposes a machine learning-based regression and classification
framework to predict the phase structures of quaternary equiatomic MEAs. A dataset of 731 alloy samples was compiled
from the literature and categorized into three phase structures: face-centered cubic (FCC, 158 samples), body-centered cubic
(BCC, 151 samples), and mixed or amorphous-like structures (none, 422 samples). Seven key features including valence electron
concentration (VEC), mixing enthalpy ( ΔH mix), atomic size difference ( δ), electronegativity difference ( Δχ), and maximum,
minimum, and average densities—were selected to establish a mathematical model describing the relationship between alloy
composition and structure. Three machine learning algorithms—random forest (RFC), support vector machine (SVC), and
extreme gradient boosting (XGBoost)—were employed, with hyperparameter optimization conducted using grid search and
ten-fold cross-validation. The results indicate that XGBoost is the best-performing model, achieving an accuracy of 0.939
on the test set, outperforming RFC (0.932) and SVC (0.721). Additionally, XGBoost demonstrated superior F1-scores, with
0.947 for the FCC class, 0.923 for BCC, and 0.943 for none, surpassing the other models. These findings confirm that XGBoost
effectively identifies key factors influencing phase stability and serves as a reliable predictive tool for the composition optimization
of MEAs.
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manuscript.docx available at https://authorea.com/users/899401/articles/1275054-smart-
alloy-design-predicting-fcc-bcc-and-amorphous-phases-in-medium-entropy-alloys-via-
interpretable-xgboost-modeling
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Posted on 7 Mar 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174132752.29405034/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.
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Posted on 7 Mar 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174132752.29405034/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.
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Posted on 7 Mar 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174132752.29405034/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.
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