Developing new high-entropy alloys with enhanced hardness using a hybrid machine learning approach: integrating interpretability and NSGA-II optimization
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OpenAlex topics: High Entropy Alloys Studies · Metal and Thin Film Mechanics · Additive Manufacturing Materials and Processes
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References (84)
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1. Zhang Y, Yang X, Liaw PK (2012) Alloy design and properties optimization of high-entropy alloys. JOM 64(7):830–838. https://doi.org/10.1007/s11837-012-0366-510.1007/s11837-012-0366-5 ↗
2. Atomic electron tomography reveals chemical order in medium- and high-entropy alloys. Nature. 2023. https://doi.org/10.1038/d41586-023-03656-510.1038/d41586-023-03656-5 ↗
3. Yan XH, Liaw PK, Zhang Y (2021) Order and disorder in amorphous and high-entropy materials. Metall Mater Trans A Phys Metall Mater Sci 52(6):2111–2122. https://doi.org/10.1007/s11661-021-06250-410.1007/s11661-021-06250-4 ↗
4. Zhang Y, New advances in high-entropy alloys. 1–7, 202010.1201/9780367374426-1 ↗
5. Yeh JW et al (2004) Nanostructured high-entropy alloys with multiple principal elements: novel alloy design concepts and outcomes. Adv Eng Mater 6(5):299–303. https://doi.org/10.1002/adem.20030056710.1002/adem.200300567 ↗
6. Yeh JW (2006) Recent progress in high-entropy alloys. Ann Chim Sci Des Mater 31(6):633–648. https://doi.org/10.3166/acsm.31.633-64810.3166/acsm.31.633-648 ↗
7. Guo S (2015) Phase selection rules for cast high entropy alloys: an overview. Mater Sci Technol (UK) 31(10):1223–1230. https://doi.org/10.1179/1743284715Y.000000001810.1179/1743284715Y.0000000018 ↗
8. Praveen S, Kim HS (2018) High-entropy alloys: potential candidates for high-temperature applications–an overview. Adv Eng Mater 20(1):1–22. https://doi.org/10.1002/adem.20170064510.1002/adem.201700645 ↗
9. Zuo T, Yang X, Liaw PK, Zhang Y (2015) Influence of Bridgman solidification on microstructures and magnetic behaviors of a non-equiatomic FeCoNiAlSi high-entropy alloy. Intermetallics 67:171–176. https://doi.org/10.1016/j.intermet.2015.08.01410.1016/j.intermet.2015.08.014 ↗
10. Zuo T et al (2017) Tailoring magnetic behavior of CoFeMnNiX (X = Al, Cr, Ga, and Sn) high entropy alloys by metal doping. Acta Mater 130:10–18. https://doi.org/10.1016/j.actamat.2017.03.01310.1016/j.actamat.2017.03.013 ↗
11. Huang S et al (2016) Mechanism of magnetic transition in FeCrCoNi-based high entropy alloys. Mater Des 103:71–74. https://doi.org/10.1016/j.matdes.2016.04.05310.1016/j.matdes.2016.04.053 ↗
12. Granberg F et al (2016) Mechanism of radiation damage reduction in equiatomic multicomponent single phase alloys. Phys Rev Lett 116(13):1–8. https://doi.org/10.1103/PhysRevLett.116.13550410.1103/PhysRevLett.116.135504 ↗
13. Zhang Y et al (2015) Influence of chemical disorder on energy dissipation and defect evolution in concentrated solid solution alloys. Nat Commun. https://doi.org/10.1038/ncomms973610.1038/ncomms9736 ↗
14. Qin Xia S, Wang Z, Fei Yang T, Zhang Y (2015) Irradiation behavior in high entropy alloys. J Iron Steel Res Int 22(10):879–884. https://doi.org/10.1016/S1006-706X(15)30084-410.1016/S1006-706X(15)30084-4 ↗
15. Xia SQ, Yang X, Yang TF, Liu S, Zhang Y (2015) Irradiation resistance in AlxCoCrFeNi high entropy alloys. JOM 67(10):2340–2344. https://doi.org/10.1007/s11837-015-1568-410.1007/s11837-015-1568-4 ↗
16. Zhou H, He J (2020) Synthesis of the new high entropy alloy and its application in energy conversion and storage. Front Energy Res 8:1–11. https://doi.org/10.3389/fenrg.2020.0007310.3389/fenrg.2020.00073 ↗
17. Nair RB, Arora HS, Mukherjee S, Singh S, Singh H, Grewal HS (2018) Exceptionally high cavitation erosion and corrosion resistance of a high entropy alloy. Ultrason Sonochem 41:252–260. https://doi.org/10.1016/j.ultsonch.2017.09.04410.1016/j.ultsonch.2017.09.044 ↗
18. Cantor B (2021) Multicomponent high-entropy Cantor alloys. Prog Mater Sci 120:100754. https://doi.org/10.1016/j.pmatsci.2020.10075410.1016/j.pmatsci.2020.100754 ↗
19. Zhang M, Zhang L, Fan J, Yu P, Li G (2019) Novel Co-free CrFeNiNb0.1Tix high-entropy alloys with ultra high hardness and strength. Mater Sci Eng A 764:138212. https://doi.org/10.1016/j.msea.2019.13821210.1016/j.msea.2019.138212 ↗
20. Ma D, Grabowski B, Körmann F, Neugebauer J, Raabe D (2015) Ab initio thermodynamics of the CoCrFeMnNi high entropy alloy: importance of entropy contributions beyond the configurational one. Acta Mater 100:90–97. https://doi.org/10.1016/j.actamat.2015.08.05010.1016/j.actamat.2015.08.050 ↗
21. Min Zheng S, Qiang Feng W, Qing Wang S (2018) Elastic properties of high entropy alloys by MaxEnt approach. Comput Mater Sci 142:332–337. https://doi.org/10.1016/j.commatsci.2017.09.06010.1016/j.commatsci.2017.09.060 ↗
22. Zhang C, Zhang F, Chen S, Cao W (2012) Computational thermodynamics aided high-entropy alloy design. JOM 64(7):839–845. https://doi.org/10.1007/s11837-012-0365-610.1007/s11837-012-0365-6 ↗
23. Debnath B, Vinoth A, Mukherjee M, Datta S (2020) Designing Fe-based high entropy alloy-a machine learning approach. In: IOP conference series: materials science and engineering, vol 912, No 5. https://doi.org/10.1088/1757-899X/912/5/05202110.1088/1757-899X/912/5/052021 ↗
24. Shahani AR, Setayeshi S, Nodamaie SA, Asadi MA, Rezaie S (2009) Prediction of influence parameters on the hot rolling process using finite element method and neural network. J Mater Process Technol 209(4):1920–1935. https://doi.org/10.1016/j.jmatprotec.2008.04.05510.1016/j.jmatprotec.2008.04.055 ↗
25. Datta S, Sil J, Banerjee MK (1999) Petri neural network model for the effect of controlled thermomechanical process parameters on the mechanical properties of HSLA steels. ISIJ Int 39(10):986–990. https://doi.org/10.2355/isijinternational.39.98610.2355/isijinternational.39.986 ↗
26. Curtarolo S, Hart GLW, Nardelli MB, Mingo N, Sanvito S, Levy O (2013) The high-throughput highway to computational materials design. Nat Mater 12(3):191–201. https://doi.org/10.1038/nmat356810.1038/nmat3568 ↗
27. Mobarak MH et al (2023) Scope of machine learning in materials research—a review. Appl Surf Sci Adv 18:100523. https://doi.org/10.1016/j.apsadv.2023.10052310.1016/j.apsadv.2023.100523 ↗
28. Li H, Yuan R, Liang H, Wang WY, Li J, Wang J (2022) Towards high entropy alloy with enhanced strength and ductility using domain knowledge constrained active learning. Mater Des 223:111186. https://doi.org/10.1016/j.matdes.2022.11118610.1016/j.matdes.2022.111186 ↗
29. Bhattacharyya JJ et al (2023) Lightweight, low cost compositionally complex multiphase alloys with optimized strength, ductility and corrosion resistance: discovery, design and mechanistic understandings. Mater Des 228:111831. https://doi.org/10.1016/j.matdes.2023.11183110.1016/j.matdes.2023.111831 ↗
30. Li S, Li S, Liu D, Zou R, Yang Z (2021) Hardness prediction of high entropy alloys with machine learning and material descriptors selection by improved genetic algorithm. Comput Mater Sci 205:111185. https://doi.org/10.1016/j.commatsci.2022.11118510.1016/j.commatsci.2022.111185 ↗
31. Dewangan SK, Samal S, Kumar V (2021) Development of an ANN-based generalized model for hardness prediction of SPSed AlCoCrCuFeMnNiW containing high entropy alloys. Mater Today Commun 27:102356. https://doi.org/10.1016/j.mtcomm.2021.10235610.1016/j.mtcomm.2021.102356 ↗
32. Rickman JM et al (2019) Materials informatics for the screening of multi-principal elements and high-entropy alloys. Nat Commun 10(1):1–10. https://doi.org/10.1038/s41467-019-10533-110.1038/s41467-019-10533-1 ↗
33. Yang C, Ren C, Jia Y, Wang G, Li M, Lu W (2022) A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness. Acta Mater 222:117431. https://doi.org/10.1016/j.actamat.2021.11743110.1016/j.actamat.2021.117431 ↗
34. Zhang Y et al (2020) Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models. Acta Mater 185:528–539. https://doi.org/10.1016/j.actamat.2019.11.06710.1016/j.actamat.2019.11.067 ↗
35. Broderick SR, Nowers JR, Narasimhan B, Rajan K (2010) Tracking chemical processing pathways in combinatorial polymer libraries via data mining. J Comb Chem 12(2):270–277. https://doi.org/10.1021/cc900145d10.1021/cc900145d ↗
36. Chowdhury A, Kautz E, Yener B, Lewis D (2016) Image driven machine learning methods for microstructure recognition. Comput Mater Sci 123:176–187. https://doi.org/10.1016/j.commatsci.2016.05.03410.1016/j.commatsci.2016.05.034 ↗
37. Dai D et al (2020) Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys. Comput Mater Sci 175:109618. https://doi.org/10.1016/j.commatsci.2020.10961810.1016/j.commatsci.2020.109618 ↗
38. Zhang H et al (2020) Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening. Acta Mater 200:803–810. https://doi.org/10.1016/j.actamat.2020.09.06810.1016/j.actamat.2020.09.068 ↗
39. Menou E et al (2018) Evolutionary design of strong and stable high entropy alloys using multi-objective optimisation based on physical models, statistics and thermodynamics. Mater Des 143:185–195. https://doi.org/10.1016/j.matdes.2018.01.04510.1016/j.matdes.2018.01.045 ↗
40. Zhang L et al (2020) Machine learning reveals the importance of the formation enthalpy and atom-size difference in forming phases of high entropy alloys. Mater Des 193:108835. https://doi.org/10.1016/j.matdes.2020.10883510.1016/j.matdes.2020.108835 ↗
41. Wen C et al (2019) Machine learning assisted design of high entropy alloys with desired property. Acta Mater 170:109–117. https://doi.org/10.1016/j.actamat.2019.03.01010.1016/j.actamat.2019.03.010 ↗
42. He Z et al (2024) Machine learning guided BCC or FCC phase prediction in high entropy alloys. J Mater Res Technol 29:3477–3486. https://doi.org/10.1016/j.jmrt.2024.01.25710.1016/j.jmrt.2024.01.257 ↗
43. Huang W, Martin P, Zhuang HL (2019) Machine-learning phase prediction of high-entropy alloys. Acta Mater 169:225–236. https://doi.org/10.1016/j.actamat.2019.03.01210.1016/j.actamat.2019.03.012 ↗
44. Senkov ON, Miller JD, Miracle DB, Woodward C (2015) Accelerated exploration of multi-principal element alloys for structural applications. Calphad Comput Coupling Phase Diagrams Thermochem 50:32–48. https://doi.org/10.1016/j.calphad.2015.04.00910.1016/j.calphad.2015.04.009 ↗
45. Lee K, Ayyasamy MV, Delsa P, Hartnett TQ, Balachandran PV (2022) Phase classification of multi-principal element alloys via interpretable machine learning. Npj Comput Mater 8(1):1–12. https://doi.org/10.1038/s41524-022-00704-y10.1038/s41524-022-00704-y ↗
46. Borg CKH et al (2020) Expanded dataset of mechanical properties and observed phases of multi-principal element alloys. Sci Data 7(1):1–6. https://doi.org/10.1038/s41597-020-00768-910.1038/s41597-020-00768-9 ↗
47. Chang H, Tao Y, Liaw PK, Ren J (2022) Phase prediction and effect of intrinsic residual strain on phase stability in high-entropy alloys with machine learning. J Alloys Compd 921:166149. https://doi.org/10.1016/j.jallcom.2022.16614910.1016/j.jallcom.2022.166149 ↗
48. Huang X, Jin C, Zhang C, Zhang H, Fu H (2021) Machine learning assisted modelling and design of solid solution hardened high entropy alloys. Mater Des 211:110177. https://doi.org/10.1016/j.matdes.2021.11017710.1016/j.matdes.2021.110177 ↗
49. Zhang M, Hector LG, Guo Y, Liu M, Qi L (2019) First-principles search for alloying elements that increase corrosion resistance of Mg with second-phase particles of transition metal impurities. Comput Mater Sci 165(12):154–166. https://doi.org/10.1016/j.commatsci.2019.04.01810.1016/j.commatsci.2019.04.018 ↗
50. Miracle DB, Senkov ON (2017) A critical review of high entropy alloys and related concepts. Acta Mater 122:448–511. https://doi.org/10.1016/j.actamat.2016.08.08110.1016/j.actamat.2016.08.081 ↗
51. Poonia A, Kishor M, Ayyagari KPR (2024) Designing of high entropy alloys with high hardness: a metaheuristic approach. Sci Rep 14(1):1–13. https://doi.org/10.1038/s41598-024-57094-y10.1038/s41598-024-57094-y ↗
52. Xiao B et al (2023) Correlations of composition, structure, and hardness in the high-entropy alloy system Nb–Mo–Ta–W. High Entropy Alloy Mater 1(1):110–119. https://doi.org/10.1007/s44210-022-00007-310.1007/s44210-022-00007-3 ↗
53. Detor A, Oppenheimer S, Casey R, Crawford C (2022) Refractory high entropy alloy dataset with room temperature ductility screening. Data Br 45:108582. https://doi.org/10.1016/j.dib.2022.10858210.1016/j.dib.2022.108582 ↗
54. Gorsse S, Nguyen MH, Senkov ON, Miracle DB (2018) Database on the mechanical properties of high entropy alloys and complex concentrated alloys. Data Br 21:2664–2678. https://doi.org/10.1016/j.dib.2018.11.11110.1016/j.dib.2018.11.111 ↗
55. Grinsztajn L, Oyallon E (2021) Why do tree-based models still outperform deep learning on tabular data?
56. Benesty J, Chen J, Huang Y (2008) On the importance of the pearson correlation coefficient in noise reduction. IEEE Trans Audio Speech Lang Process 16(4):757–765. https://doi.org/10.1109/TASL.2008.91907210.1109/TASL.2008.919072 ↗
57. Xu W, Yuan Z, Liu Z (2024) Feature selection for unbalanced distribution hybrid data based on k-nearest neighborhood rough set. IEEE Trans Artif Intell 5(1):229–243. https://doi.org/10.1109/TAI.2023.323720310.1109/TAI.2023.3237203 ↗
58. Liang J, Hou L, Luan Z, Huang W (2019) Feature selection with conditional mutual information considering feature interaction. Symmetry 11(7):858. https://doi.org/10.3390/sym1107085810.3390/sym11070858 ↗
59. Zhang Y, Zhang Z (2012) Feature subset selection with cumulate conditional mutual information minimization. Expert Syst Appl 39(5):6078–6088. https://doi.org/10.1016/j.eswa.2011.12.00310.1016/j.eswa.2011.12.003 ↗
60. Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 2017:4766–4775
61. Greenwell BM, Boehmke BC, McCarthy AJ (2018) A simple and effective model-based variable importance measure. 1–2710.32614/CRAN.package.vip ↗
62. Hume-Rothery W (1967) Comments on papers resulting from Hume-Rothery’s note—1965. Acta Metall 15(3):567–56910.1016/0001-6160(67)90091-0 ↗
63. Nong ZS, Zhu JC, Cao Y, Yang XW, Lai ZH, Liu Y (2014) Stability and structure prediction of cubic phase in as cast high entropy alloys. Mater Sci Technol (UK) 30(3):363–369. https://doi.org/10.1179/1743284713Y.000000036810.1179/1743284713Y.0000000368 ↗
64. Singh S, Katiyar NK, Goel S, Joshi SN (2023) Phase prediction and experimental realisation of a new high entropy alloy using machine learning. Sci Rep 13(1):1–17. https://doi.org/10.1038/s41598-023-31461-710.1038/s41598-023-31461-7 ↗
65. Tong CJ et al (2005) Microstructure characterization of AlxCoCrCuFeNi high-entropy alloy system with multiprincipal elements. Metall Mater Trans A Phys Metall Mater Sci 36(4):881–893. https://doi.org/10.1007/s11661-005-0283-010.1007/s11661-005-0283-0 ↗
66. Ouyang R, Curtarolo S, Ahmetcik E, Scheffler M, Ghiringhelli LM (2018) SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys Rev Mater 2(8):1–11. https://doi.org/10.1103/PhysRevMaterials.2.08380210.1103/PhysRevMaterials.2.083802 ↗
67. Wen C et al (2021) Modeling solid solution strengthening in high entropy alloys using machine learning. Acta Mater 212:116917. https://doi.org/10.1016/j.actamat.2021.11691710.1016/j.actamat.2021.116917 ↗
68. Ye YF, Wang Q, Lu J, Liu CT, Yang Y (2016) High-entropy alloy: challenges and prospects. Mater Today 19(6):349–362. https://doi.org/10.1016/j.mattod.2015.11.02610.1016/j.mattod.2015.11.026 ↗
69. Gao MC, Liaw PK, Yeh JW, Zhang Y (2016) High-entropy alloys: fundamentals and applications. Springer, Berlin. https://doi.org/10.1007/978-3-319-27013-510.1007/978-3-319-27013-5 ↗
70. Miedema AR, De Châtel PF, De Boer FR (1980) Cohesion in alloys-fundamentals of a semi-empirical model. Phys B+C 100(1):1–28. https://doi.org/10.1016/0378-4363(80)90054-610.1016/0378-4363(80)90054-6 ↗
71. Oh HS et al (2019) Engineering atomic-level complexity in high-entropy and complex concentrated alloys. Nat Commun 10(1):1–8. https://doi.org/10.1038/s41467-019-10012-710.1038/s41467-019-10012-7 ↗
72. Shafiei A (2022) Simple approach to model the strength of solid-solution high entropy alloys in Co-Cr-Fe-Mn-Ni system. Strength Mater 54(4):705–716. https://doi.org/10.1007/s11223-022-00448-610.1007/s11223-022-00448-6 ↗
73. Singh P, Smirnov AV, Johnson DD (2015) Atomic short-range order and incipient long-range order in high-entropy alloys. Phys Rev B Condens Matter Mater Phys 91(22):1–12. https://doi.org/10.1103/PhysRevB.91.22420410.1103/PhysRevB.91.224204 ↗
74. Pettinari F et al (2001) Local order in industrial and model γ phases of superalloys. Acta Mater 49(13):2549–2556. https://doi.org/10.1016/S1359-6454(01)00103-310.1016/S1359-6454(01)00103-3 ↗
75. Seol JB, Ko WS, Sohn SS et al (2022) Mechanically derived short-range order and its impact on the multi-principal-element alloys. Nat Commun 13:6766. https://doi.org/10.1038/s41467-022-34470-810.1038/s41467-022-34470-8 ↗
76. Yang X, Zhang Y (2012) Prediction of high-entropy stabilized solid-solution in multi-component alloys. Mater Chem Phys 132(2–3):233–238. https://doi.org/10.1016/j.matchemphys.2011.11.02110.1016/j.matchemphys.2011.11.021 ↗
77. Wu CS, Tsai PH, Kuo CM, Tsai CW (2018) Effect of atomic size difference on the microstructure and mechanical properties of high-entropy alloys. Entropy 20(12):967. https://doi.org/10.3390/e2012096710.3390/e20120967 ↗
78. Zhang Y, Wu H, Yu X, Tang D (2022) Role of Cr in the high-temperature oxidation behavior of CrxMnFeNi high-entropy alloys at 800 °C in air. Corros Sci 200:110211. https://doi.org/10.1016/j.corsci.2022.11021110.1016/j.corsci.2022.110211 ↗
79. Tsai MH, Yeh JW (2014) High-entropy alloys: a critical review. Mater Res Lett 2(3):107–123. https://doi.org/10.1080/21663831.2014.91269010.1080/21663831.2014.912690 ↗
80. Tung CC, Yeh JW, Tsung Shun T, Chen SK, Huang YS, Chen HC (2007) On the elemental effect of AlCoCrCuFeNi high-entropy alloy system. Mater Lett 61(1):1–5. https://doi.org/10.1016/j.matlet.2006.03.14010.1016/j.matlet.2006.03.140 ↗
81. Wu JM, Lin SJ, Yeh JW, Chen SK, Huang YS, Chen HC (2006) Adhesive wear behavior of AlxCoCrCuFeNi high-entropy alloys as a function of aluminum content. Wear 261(5–6):513–519. https://doi.org/10.1016/j.wear.2005.12.00810.1016/j.wear.2005.12.008 ↗
82. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.99601710.1109/4235.996017 ↗
83. Xu P, Ji X, Li M, Lu W (2023) Small data machine learning in materials science. Npj Comput Mater 9(1):1–15. https://doi.org/10.1038/s41524-023-01000-z10.1038/s41524-023-01000-z ↗
84. Ma Y, Li M, Mu Y, Wang G, Lu W (2023) Accelerated design for high-entropy alloys based on machine learning and multiobjective optimization. J Chem Inf Model 63(19):6029–6042. https://doi.org/10.1021/acs.jcim.3c0091610.1021/acs.jcim.3c00916 ↗