Acta Metallurgica Sinica (English Letters) ›› 2025, Vol. 38 ›› Issue (8): 1261-1274.DOI: 10.1007/s40195-025-01876-4
Special Issue: 高熵合金专辑 2025年 ； 机器学习专辑 2025
Jiayu Wang1, Ke Liu2, Zhao Lei1, Xing Li2, Li Liu1(
), Sujun Wu2()
Received:2024-12-02
Revised:2025-01-24
Accepted:2025-02-13
Online:2025-08-10
Published:2025-05-24
Contact:
Li Liu, Sujun Wu
Jiayu Wang, Ke Liu, Zhao Lei, Xing Li, Li Liu, Sujun Wu. Machine-Learning-Assisted Phase Prediction in High-Entropy Alloys Using Two-Step Feature Selection Strategy[J]. Acta Metallurgica Sinica (English Letters), 2025, 38(8): 1261-1274.
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| Features | Formula |
|---|---|
| Average atomic radius | $a= \sum_{i=1}^{n}{c}_{i}{r}_{i}$ |
| Atomic size difference | $\delta = \sqrt{\sum_{i=1}^{n}{c}_{i}{(1-\frac{{r}_{i}}{a})}^{2}}$ |
| Melting point | ${T}_{\text{m}}= \sum_{i=1}^{n}{c}_{i}{T}_{i}$ |
| Standard deviation of melting point | ${\sigma }_{T}= \sqrt{\sum_{i=1}^{n}{c}_{i}{(1-\frac{{T}_{i}}{{T}_{\text{m}}})}^{2}}$ |
| Mixing enthalpy | $\Delta {H}_{\text{mix}}=\sum_{i=1, i\ne j}^{n}4{H}_{ij}{c}_{i}{c}_{j}$ |
| Standard deviation of mixing enthalpy | ${\sigma }_{\Delta H}= \sqrt{\sum_{i=1, i\ne j}^{n}{c}_{i}{c}_{j}{({H}_{ij}-\Delta {H}_{\text{mix}})}^{2}}$ |
| Mixing entropy | $\Delta {S}_{\text{mix}}= -R\sum_{i=1}^{n}{c}_{i}\text{ln}{c}_{i}$ |
| Electronegativity | $\chi = \sum_{i=1}^{n}{c}_{i}{\chi }_{i}$ |
| Electronegativity difference | $\Delta \chi = \sqrt{\sum_{i=1}^{n}{c}_{i}{({\chi }_{i}-\chi )}^{2}}$ |
| Valence electron concentration | $\text{VEC}= \sum_{i=1}^{n}{c}_{i}{\text{VEC}}_{i}$ |
| Standard deviation of valence electron concentration | ${\sigma }_{\text{VEC}}= \sqrt{\sum_{I=1}^{n}{c}_{i}{({\text{VEC}}_{i}-\text{VEC})}^{2}}$ |
Table 1 Eleven features selected for building machine learning models
| Features | Formula |
|---|---|
| Average atomic radius | $a= \sum_{i=1}^{n}{c}_{i}{r}_{i}$ |
| Atomic size difference | $\delta = \sqrt{\sum_{i=1}^{n}{c}_{i}{(1-\frac{{r}_{i}}{a})}^{2}}$ |
| Melting point | ${T}_{\text{m}}= \sum_{i=1}^{n}{c}_{i}{T}_{i}$ |
| Standard deviation of melting point | ${\sigma }_{T}= \sqrt{\sum_{i=1}^{n}{c}_{i}{(1-\frac{{T}_{i}}{{T}_{\text{m}}})}^{2}}$ |
| Mixing enthalpy | $\Delta {H}_{\text{mix}}=\sum_{i=1, i\ne j}^{n}4{H}_{ij}{c}_{i}{c}_{j}$ |
| Standard deviation of mixing enthalpy | ${\sigma }_{\Delta H}= \sqrt{\sum_{i=1, i\ne j}^{n}{c}_{i}{c}_{j}{({H}_{ij}-\Delta {H}_{\text{mix}})}^{2}}$ |
| Mixing entropy | $\Delta {S}_{\text{mix}}= -R\sum_{i=1}^{n}{c}_{i}\text{ln}{c}_{i}$ |
| Electronegativity | $\chi = \sum_{i=1}^{n}{c}_{i}{\chi }_{i}$ |
| Electronegativity difference | $\Delta \chi = \sqrt{\sum_{i=1}^{n}{c}_{i}{({\chi }_{i}-\chi )}^{2}}$ |
| Valence electron concentration | $\text{VEC}= \sum_{i=1}^{n}{c}_{i}{\text{VEC}}_{i}$ |
| Standard deviation of valence electron concentration | ${\sigma }_{\text{VEC}}= \sqrt{\sum_{I=1}^{n}{c}_{i}{({\text{VEC}}_{i}-\text{VEC})}^{2}}$ |
Fig. 1 Data visualization. a Pair plot showing the relationship between phase structure and all design parameters in the 296 datasets. b Hexagonal binning for $\text{VEC}$ parameter used for phase structure prediction
Fig. 2 First step feature selection. a Pearson correlation coefficient with eleven features. b Cross-validation accuracy with $\text{VEC}$ or $a$ feature
Fig. 3 Second step feature selection. a Importance ranking of ten features using RF model. The cross-validation accuracy of b MLP, c SVM, d DT, e RF, and f KNN model with various features
| Model | Selected features |
|---|---|
| MLP | $\text{VEC}$ |
| SVM | $\text{VEC}$, ${T}_{\text{m}}$, ${\sigma }_{\text{VEC}}$, ${\sigma }_{T}$, $\chi $, $\Delta \chi $, $\delta $, $\Delta {H}_{\text{mix}}$ |
| DT | $\text{VEC}$, ${T}_{\text{m}}$ |
| RF | $\text{VEC}$, ${T}_{\text{m}}$, ${\sigma }_{\text{VEC}}$ |
| KNN | $\text{VEC}$, ${T}_{\text{m}}$ |
Table 2 Finally selected features for building machine learning models
| Model | Selected features |
|---|---|
| MLP | $\text{VEC}$ |
| SVM | $\text{VEC}$, ${T}_{\text{m}}$, ${\sigma }_{\text{VEC}}$, ${\sigma }_{T}$, $\chi $, $\Delta \chi $, $\delta $, $\Delta {H}_{\text{mix}}$ |
| DT | $\text{VEC}$, ${T}_{\text{m}}$ |
| RF | $\text{VEC}$, ${T}_{\text{m}}$, ${\sigma }_{\text{VEC}}$ |
| KNN | $\text{VEC}$, ${T}_{\text{m}}$ |
Fig. 4 Cross-validation accuracy of a MLP, b SVM, c DT, d RF, and e KNN model containing a subset of initial 11 features
Fig. 5 Hyperparameter optimization of a MLP, b SVM, c RF, d DT, and e KNN model. f The cross-validation accuracy after optimization
| Model | Hyperparameters |
|---|---|
| MLP | First layer number = 5, Second layer number = 9 |
| SVM | C = 13, γ = 1 |
| DT | Max depth = 4 |
| RF | Max depth = 9, Estimators = 47 |
| KNN | Neighbors = 1 |
Table 3 Hyperparameters of five machine learning models after optimization
| Model | Hyperparameters |
|---|---|
| MLP | First layer number = 5, Second layer number = 9 |
| SVM | C = 13, γ = 1 |
| DT | Max depth = 4 |
| RF | Max depth = 9, Estimators = 47 |
| KNN | Neighbors = 1 |
Fig. 6 a Prediction accuracy of traditional $\text{VEC}$ criterion and machine learning methods in 296 datasets. Confusion matrixes of b MLP, c SVM, d DT, e RF, and f KNN model for testing dataset
Fig. 7 Parallel coordinate plot shows the distribution of final selected features for BCC, FCC, and BCC + FCC phase structure
| Composition | VEC | MLP | SVM | DT | RF | KNN | Experiment |
|---|---|---|---|---|---|---|---|
| Al1CoCu6Ni6Fe6 | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| Al3CoCu6Ni6Fe6 | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| Al6CoCu6Ni6Fe6 | FCC | FCC | BCC + FCC | FCC | BCC + FCC | BCC + FCC | BCC + FCC |
| CoCuFeNi | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| CoCuFeNi5 | FCC | FCC | FCC + BCC | FCC | FCC + BCC | FCC | FCC + BCC |
| CoCuFeNi10 | FCC | FCC | FCC | FCC | FCC + BCC | FCC | FCC + BCC |
| CoCuFeNi15 | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| CoCuFeNi20 | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| Ta0.45Nb0.35Zr0.10Ti0.10 | BCC | BCC | BCC | BCC | BCC | BCC | BCC |
| Ta0.60Nb0.15Zr0.05Ti0.20 | BCC | BCC | BCC + FCC | BCC | BCC | BCC | BCC |
| Ta0.05Nb0.70Zr0.15Ti0.10 | BCC | BCC | BCC | BCC | BCC | BCC | BCC |
| Ta0.40Nb0.30Zr0.05Ti0.25 | BCC | BCC | BCC | BCC | BCC | BCC | BCC |
| Ta0.05Nb0.65Zr0.05Ti0.25 | BCC | BCC | BCC + FCC | BCC | BCC | BCC | BCC |
Table 4 Experimental and prediction results of the phase structure of AlxCoCu6Ni6Fe6 (x = 1, 3, 6) HEAs with various machine learning models and $\text{VEC}$ criterion
| Composition | VEC | MLP | SVM | DT | RF | KNN | Experiment |
|---|---|---|---|---|---|---|---|
| Al1CoCu6Ni6Fe6 | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| Al3CoCu6Ni6Fe6 | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| Al6CoCu6Ni6Fe6 | FCC | FCC | BCC + FCC | FCC | BCC + FCC | BCC + FCC | BCC + FCC |
| CoCuFeNi | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| CoCuFeNi5 | FCC | FCC | FCC + BCC | FCC | FCC + BCC | FCC | FCC + BCC |
| CoCuFeNi10 | FCC | FCC | FCC | FCC | FCC + BCC | FCC | FCC + BCC |
| CoCuFeNi15 | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| CoCuFeNi20 | FCC | FCC | FCC | FCC | FCC | FCC | FCC |
| Ta0.45Nb0.35Zr0.10Ti0.10 | BCC | BCC | BCC | BCC | BCC | BCC | BCC |
| Ta0.60Nb0.15Zr0.05Ti0.20 | BCC | BCC | BCC + FCC | BCC | BCC | BCC | BCC |
| Ta0.05Nb0.70Zr0.15Ti0.10 | BCC | BCC | BCC | BCC | BCC | BCC | BCC |
| Ta0.40Nb0.30Zr0.05Ti0.25 | BCC | BCC | BCC | BCC | BCC | BCC | BCC |
| Ta0.05Nb0.65Zr0.05Ti0.25 | BCC | BCC | BCC + FCC | BCC | BCC | BCC | BCC |
Fig. 8 XRD profiles of AlxCoCu6Ni6Fe6 (x = 1, 3, 6) HEAs
Fig. 9 Backscattered electron micrographs of AlxCoCu6Ni6Fe6 HEAs: a x = 1, b x = 3, c x = 6; distribution of Cu element in AlxCoCu6Ni6Fe6 HEAs: d x = 1, e x = 3, f x = 6
| $\Delta {H}_{\text{mix}}^{ij}$ (kJ/mol) | Al | Co | Cu | Ni | Fe |
|---|---|---|---|---|---|
| Al | - | − 19 | − 1 | − 22 | − 11 |
| Co | − 19 | - | 6 | 0 | − 1 |
| Cu | − 1 | 6 | - | 4 | 13 |
| Ni | − 22 | 0 | 4 | - | − 2 |
| Fe | − 11 | − 1 | 13 | − 2 | - |
Table 5 Binary mixing enthalpies of atomic pairs in AlxCoCu6Ni6Fe6 (x = 1, 3, 6) HEAs
| $\Delta {H}_{\text{mix}}^{ij}$ (kJ/mol) | Al | Co | Cu | Ni | Fe |
|---|---|---|---|---|---|
| Al | - | − 19 | − 1 | − 22 | − 11 |
| Co | − 19 | - | 6 | 0 | − 1 |
| Cu | − 1 | 6 | - | 4 | 13 |
| Ni | − 22 | 0 | 4 | - | − 2 |
| Fe | − 11 | − 1 | 13 | − 2 | - |
Fig. 10 EBSD images of AlxCoCu6Ni6Fe6 (x = 1, 3, 6) HEAs. a, d Image quality (IQ) map and grain size statistics diagram of Al1CoCu6Ni6Fe6 HEA. b, e IQ image and grain size statistics diagram of Al3CoCu6Ni6Fe6 HEA. c IQ image and f grain size statistics diagram and g, h phase maps of Al6CoCu6Ni6Fe6 HEA
Fig. 11 a Engineering stress-strain curves of AlxCoCu6Ni6Fe6 (x = 1, 3, 6) HEAs. b Potentiodynamic polarization curves of AlxCoCu6Ni6Fe6 HEAs in 3.5% NaCl solution
Yield strength (MPa) | Tensile strength (MPa) | Elongation (%) | Hardness (HV) | |
|---|---|---|---|---|
| Al1CoCu6Ni6Fe6 | 517 ± 5 | 732 ± 17 | 20.4 ± 4.1 | 169 ± 3 |
| Al3CoCu6Ni6Fe6 | 654 ± 10 | 722 ± 12 | 1.8 ± 0.01 | 230 ± 5 |
| Al6CoCu6Ni6Fe6 | - | 509 ± 40 | 1.5 ± 0.3 | 513 ± 5 |
Table 6 Mechanical properties of AlxCoCu6Ni6Fe6 HEAs
Yield strength (MPa) | Tensile strength (MPa) | Elongation (%) | Hardness (HV) | |
|---|---|---|---|---|
| Al1CoCu6Ni6Fe6 | 517 ± 5 | 732 ± 17 | 20.4 ± 4.1 | 169 ± 3 |
| Al3CoCu6Ni6Fe6 | 654 ± 10 | 722 ± 12 | 1.8 ± 0.01 | 230 ± 5 |
| Al6CoCu6Ni6Fe6 | - | 509 ± 40 | 1.5 ± 0.3 | 513 ± 5 |
${I}_{\text{corr}}$ (μA/cm2) | ${E}_{\text{corr}}$ (mv) | |
|---|---|---|
| Al1CoCu6Ni6Fe6 | 0.206 ± 0.03 | -314 ± 19.6 |
| Al3CoCu6Ni6Fe6 | 0.370 ± 0.10 | -334 ± 54.6 |
| Al6CoCu6Ni6Fe6 | 0.649 ± 0.20 | -280 ± 34.1 |
Table 7 Electrochemical parameters of AlxCoCu6Ni6Fe6 HEAs in 3.5% NaCl solution
${I}_{\text{corr}}$ (μA/cm2) | ${E}_{\text{corr}}$ (mv) | |
|---|---|---|
| Al1CoCu6Ni6Fe6 | 0.206 ± 0.03 | -314 ± 19.6 |
| Al3CoCu6Ni6Fe6 | 0.370 ± 0.10 | -334 ± 54.6 |
| Al6CoCu6Ni6Fe6 | 0.649 ± 0.20 | -280 ± 34.1 |
Fig. 12 Tensile fracture morphology of AlxCoCu6Ni6Fe6 HEAs: a x = 1, b x = 3, c x = 6. Surface morphology of AlxCoCu6Ni6Fe6 HEAs after potentiodynamic polarization test in 3.5% NaCl solution: d x = 1, e x = 3, f x = 6
| [1] | P.K. Huang, J.W. Yeh, T.T. Shun, S.K. Chen, Adv. Eng. Mater. 6, 74 (2004) |
| [2] | B. Cantor, I.T.H. Chang, P. Knight, A.J.B. Vincent, Mater. Sci. Eng. A 375-377, 213 (2004) |
| [3] | D.B. Miracle, O.N. Senkov, Acta Mater. 122, 448 (2017) |
| [4] |
O.N. Senkov, J.D. Miller, D.B. Miracle, C. Woodward, Nat. Commun. 6, 6529 (2015)
DOI PMID |
| [5] | Z. Li, K.G. Pradeep, Y. Deng, D. Raabe, C.C. Tasan, Nature 534, 227 (2016) |
| [6] | T.T. Shun, Y.C. Du, J. Alloys Compd. 479, 157 (2009) |
| [7] | Y.Z. Shi, B. Yang, X. Xie, J. Brechtl, K.A. Dahmen, P.K. Liaw, Corros. Sci. 119, 33 (2017) |
| [8] | M. Todai, T. Nagase, T. Hori, A. Matsugaki, A. Sekita, T. Nakano, Scr. Mater. 129, 65 (2017) |
| [9] | T. Hori, T. Nagase, M. Todai, A. Matsugaki, T. Nakano, Scr. Mater. 172, 83 (2019) |
| [10] | S.Q. Xia, Z. Wang, T.F. Yang, Y. Zhang, J. Iron Steel Int. 22, 879 (2015) |
| [11] | I.S. Wani, T. Bhattacharjee, S. Sheikh, Y.P. Lu, S. Chatterjee, P.P. Bhattacharjee, S. Guo, N. Tsuji, Mater. Res. Lett. 4, 174 (2016) |
| [12] | D.J.M. King, S.C. Middleburgh, A.G. McGregor, M.B. Cortie, Acta Mater. 104, 172 (2016) |
| [13] | A. Abu-Odeh, E. Galvan, T. Kirk, H. Mao, Q. Chen, P. Mason, R. Malak, R. Arroyave, Acta Mater. 152, 41 (2018) |
| [14] | Y.F. Ye, Q. Wang, J. Lu, C.T. Liu, Y. Yang, Mater. Today 19, 349 (2016) |
| [15] | S. Guo, C. Ng, J. Lu, C.T. Liu, J. Appl. Phys. 109, 103505 (2011) |
| [16] | Y. Zhang, Y.J. Zhou, J.P. Lin, G.L. Chen, P.K. Liaw, Adv. Eng. Mater. 10, 534 (2008) |
| [17] | L. Zhang, H.M. Chen, X.M. Tao, H.G. Cai, J.N. Liu, Y.F. Ouyang, Q. Peng, Y. Dong, Mater. Des. 193, 108835 (2020) |
| [18] | U.K. Jaiswal, Y.V. Krishna, M.R. Rahul, G. Phanikumar, Comp. Mater. Sci. 197, 110623 (2021) |
| [19] | C. Wen, Y. Zhang, C.X. Wang, D.Z. Xue, Y. Bai, S. Antonov, L.H. Dai, T. Lookman, Y.J. Su, Acta Mater. 170, 109 (2019) |
| [20] | X. Li, C.L. Wang, L.C. Zhang, S.F. Zhou, J. Huang, M.Y. Gao, C. Liu, M. Huang, Y.T. Zhu, H. Chen, J.Y. Zhang, Z.J. Tan, Acta Metall. Sin.-Engl. Lett. 37, 1858 (2024) |
| [21] | Y.V. Krishna, U.K. Jaiswal, M.R. Rahul, Scr. Mater. 197, 113804 (2021) |
| [22] | X.Y. Huang, C. Jin, C. Zhang, H. Zhang, H.W. Fu, Mater. Des. 211, 110177 (2021) |
| [23] |
S. Gorsse, M.H. Nguyen, O.N. Senkov, D.B. Miracle, Data Brief 21, 2664 (2018)
DOI PMID |
| [24] | R. Machaka, G.T. Motsi, L.M. Raganya, P.M. Radingoana, S. Chikosha, Data Brief 38, 107346 (2021) |
| [25] | Z.Q. Zhou, Y.J. Zhou, Q.F. He, Z.Y. Ding, F.C. Li, Y. Yang, NPJ Comput. Mater. 5, 128 (2019) |
| [26] | D.H. Wolpert, W.G. Macready, IEEE Trans. Evolut. Comput. 1, 67 (1997) |
| [27] | X. Jiang, H. Yin, C. Zhang, R. Zhang, K. Zhang, Z. Deng, G. Liu, X. Qu, Comput. Mater. Sci. 143, 295 (2018) |
| [28] | P. Raccuglia, K. Elbert, P.D.F. Adler, C. Falk, M. Wenny, A. Mollo, M. Zeller, S.A. Friedler, J. Schrier, A. Norquist, Nature 553, 73 (2016) |
| [29] | V. Stanev, C. Oses, A.G. Kusne, E. Rodriguez, J. Paglione, S. Curtarolo, I. Takeuchi, NPJ Comput. Mater. 4, 29 (2018) |
| [30] | H.N. Chang, Y.W. Tao, P.K. Liaw, J.L. Ren, J. Alloys Compd. 921, 166149 (2022) |
| [31] |
J. Xiong, S.Q. Shi, T.Y. Zhang, J. Mater. Sci. Technol. 87, 133 (2021)
DOI |
| [32] | H.T. Zhang, H.D. Fu, X.Q. He, C.S. Wang, L. Jiang, L.Q. Chen, J.X. Xie, Acta Mater. 200, 803 (2020) |
| [33] | Z.H. Zhu, W.H. Ning, X.Y. Niu, Y.H. Zhao, Acta Metall. Sin.-Engl. Lett. 37, 453 (2024) |
| [34] | X.Y. Teng, J.C. Pang, F. Liu, C.L. Zou, X. Bai, S.X. Lin, Z.F. Zhang, Acta Metall. Sin.-Engl. Lett. 36, 1536 (2023) |
| [35] | X.L. Wang, X. Ji, B. He, D.P. Wang, C.N. Li, Y.C. Liu, W. Guan, L. Cui, Acta Metall. Sin.-Engl. Lett. 36, 573 (2023) |
| [36] | Z.H. Li, L. Qin, B.S. Guo, J.P. Yuan, Z.G. Zhang, W. Li, J.W. Mi, Acta Metall. Sin.-Engl. Lett. 35, 115 (2022) |
| [37] | Y. Sun, Z.C. Lu, X.J. Liu, Q. Du, H.M. Xie, J.C. Lv, R.X. Song, Y. Wu, H. Wang, S.H. Jiang, Z.P. Lu, Appl. Phys. Lett. 119, 201905 (2021) |
| [38] | J.F. Zhang, H. Qiu, H.G. Zhu, Z.H. Xie, Mater. Charact. 175, 111091 (2021) |
| [39] | T. Nguyen, M. Huang, H.J. Li, L. Hong, S. Yang, Mater. Sci. Eng. A 832, 142495 (2022) |
| [40] | H.Q. Wang, K.F. Lu, S.C. Fan, Y.F. Liu, Y.F. Zhao, F.S. Yin, Mater. Today Commun. 32, 103918 (2022) |
| [41] | J.W. Yeh, S.K. Chen, S.J. Lin, J.Y. Gan, T.S. Chin, T.T. Shun, C.H. Tsau, S.Y. Chang, Adv. Eng. Mater. 6, 299 (2004) |
| [42] | C.M. Lin, H.L. Tsai, J. Alloys Compd. 489, 30 (2010) |
| [43] | J. Moon, J.M. Park, J.W. Bae, H.S. Do, B.J. Lee, H.S. Kim, Acta Mater. 193, 71 (2020) |
| [44] | F.C. Zhao, X.M. Zhao, R.D. Zhao, F.F. Wu, S.H. Chen, Intermetallics 178, 108627 (2025) |
| [45] | X.Y. Gu, Y.X. Zhuang, P. Jia, Mater. Sci. Eng. A 840, 142983 (2022) |
| [46] | W.R. Wang, W.L. Wang, S.C. Wang, Y.C. Tsai, C.H. Lai, J.W. Yeh, Intermetallics 26, 44 (2012) |
| [47] | R. Hu, J.H. Du, Y.J. Zhang, Q.X. Ji, R.R. Zhang, J.X. Chen, J. Alloys Compd. 921, 165455 (2022) |
| [48] | X.R. Zhang, J. Guo, X.H. Zhang, Y.P. Song, Z.X. Li, X.F. Xing, D. Kong, J. Alloys Compd. 775, 565 (2019) |
| [49] | Y.J. Hsu, W.C. Chiang, J.K. Wu, Mater. Chem. Phys. 92, 112 (2005) |
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