Acta Metallurgica Sinica (English Letters) ›› 2025, Vol. 38 ›› Issue (8): 1261-1274.DOI: 10.1007/s40195-025-01876-4
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:2025-05-24
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.
Add to citation manager EndNote|Ris|BibTeX
| 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) |
| [1] | Biao Zhang, Yuntian Du, Huishuang Jia, Yuanyi Zhou, Liguang Wang, Minghe Zhang, Yunli Feng, Weimin Gao, Ning Xu. Hot Deformation Behavior of CoNiV Medium-Entropy Alloy: Constitutive Model, Convolutional Neural Network, Hot Processing Map, and Microstructure Evolution [J]. Acta Metallurgica Sinica (English Letters), 2025, 38(8): 1275-1292. |
| [2] | Xiaolong Pei, Hua Hou, Yuhong Zhao. A Review of Intelligent Design and Optimization of Metal Casting Processes [J]. Acta Metallurgica Sinica (English Letters), 2025, 38(8): 1293-1311. |
| [3] | Zhongxue Wang, Le Ren, Yating Zhang, Mengcheng Zhou, Xinfang Zhang. Realizing Ultra-fast Spheroidization of GCr15 Bearing Steel by Analyzing the Correlation of Carbide Dissolution Law and Pulsed Electric Current Parameters Through Machine Learning [J]. Acta Metallurgica Sinica (English Letters), 2025, 38(7): 1207-1218. |
| [4] | Meisa Zhou, Kun-Ming Pan, Xiao-Ye Zhou, Shulong Ye, Shaojie Du, Hong-Hui Wu. Surface Wear Behavior of Nanograined NbMoTaW Refractory High-Entropy Alloys via Nano-scratching Simulations [J]. Acta Metallurgica Sinica (English Letters), 2025, 38(6): 946-960. |
| [5] | Hong Chen, Ruitao Qu, Haotian Ma, Kexing Song, Feng Liu. Simultaneously Enhanced Strength and Fracture Resistance in HfNbTaTiZr Refractory High-Entropy Alloy at Higher Strain Rate [J]. Acta Metallurgica Sinica (English Letters), 2025, 38(4): 529-541. |
| [6] | Jingxiang Xu, Yuyan Yang, Ruiyang Huang, Xinwei Yuan, Huakang Bian, Zhenhua Chu, Yang Wang, Yanhua Lei. Enhancing the Corrosion Resistance of FeNiCoCrW0.2Al0.1 High-Entropy Alloy in 3.5 wt% NaCl Solution by Bilayer Passivation [J]. Acta Metallurgica Sinica (English Letters), 2025, 38(3): 407-418. |
| [7] | Lingxiao Du, Hang Ding, Yun Xie, Li Ji, Wanbin Chen, Yunze Xu. Effect of Laser Energy Density on Microstructures and Properties of Additively Manufactured AlCoCrFeNi2.1 Eutectic High-Entropy Alloy [J]. Acta Metallurgica Sinica (English Letters), 2025, 38(2): 233-244. |
| [8] | Hongwei Yan, Yong’an Zhang, Wei Xiao, Boyu Xue, Rui Liu, Xiwu Li, Zhihui Li, Baiqing Xiong. Experimental and DFT Investigations of AlNbTiVZr High Entropy Alloys with Excellent Mechanical Properties [J]. Acta Metallurgica Sinica (English Letters), 2024, 37(9): 1480-1490. |
| [9] | Zirui Chen, Liyuan Wang, Jiayu Zhao, Guanhua Cui, Zhuo Gao, Zhiyuan Fan, Xiaohui Shi, Junwei Qiao. Microstructure and Mechanical Properties of the Ti62Nb12Mo12Ta12W2 Refractory High Entropy Alloy Prepared through Spark Plasma Sintering [J]. Acta Metallurgica Sinica (English Letters), 2024, 37(8): 1387-1398. |
| [10] | Lan Zhang, Dao-Kui Xu, Bao-Jie Wang, Cui-Lan Lu, Shuo Wang, Xiang-Bo Xu, Dong-Liang Wang, Xin Lv, En-Hou Han. Mechanical Behavior and Failure Mechanism of an As-Extruded Mg-11wt%Y Alloy at Elevated Temperature [J]. Acta Metallurgica Sinica (English Letters), 2024, 37(6): 969-981. |
| [11] | Hong-Wei Zhang, Li-Wei Lan, Zhe-Yu Yang, Chang-Chun Li, Wen-Xian Wang. Microstructure Evolution and Nanomechanical Behavior of Micro-Area in Molten Pool of Selective Laser Melting (CoCrNi)82Al9Ti9 High-Entropy Alloy [J]. Acta Metallurgica Sinica (English Letters), 2024, 37(6): 1019-1033. |
| [12] | Huanzhi Zhang, Tianxin Li, Qianqian Wang, Zhenbo Zhu, Hefei Huang, Yiping Lu. Effects of Nitrogen Doping on Microstructures and Irradiation Resistance of Ti-Zr-Nb-V-Mo Refractory High-Entropy Alloy [J]. Acta Metallurgica Sinica (English Letters), 2024, 37(6): 1007-1018. |
| [13] | Xiaoliang Jia, Guhui Gao, Xiaolu Gui, Chun Feng, R. D. K. Misra, Bingzhe Bai. Uncovering Microstructure-Property Relationship in Ni-Alloyed Fe-Mn-Al-C Low-Density Steel Treated by Hot-Rolling and Air-Cooling Process [J]. Acta Metallurgica Sinica (English Letters), 2024, 37(4): 713-725. |
| [14] | Xuan-Hong Cai, Zhen-Hua Wang, Ben Niu, Jin-Feng Li, Qing Wang, Show authors. Microstructural Evolutions and Mechanical Properties of Energetic Al1 (TiZrNbTaMoCr)15 High-Entropy Alloys [J]. Acta Metallurgica Sinica (English Letters), 2024, 37(4): 620-632. |
| [15] | Zhihong Zhu, Wenhang Ning, Xuanyang Niu, Yuhong Zhao. Machine Learning-Based Research on Tensile Strength of SiC-Reinforced Magnesium Matrix Composites via Stir Casting [J]. Acta Metallurgica Sinica (English Letters), 2024, 37(3): 453-466. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||
WeChat
