J. Mater. Sci. Technol. ›› 2021, Vol. 68: 70-75.DOI: 10.1016/j.jmst.2020.08.008
• Research Article • Previous Articles Next Articles
Jia Lia, Baobin Xiea, Qihong Fanga,*(), Bin Liub, Yong Liub, Peter K. Liawc
Received:
2020-04-16
Revised:
2020-05-22
Accepted:
2020-06-04
Published:
2021-03-30
Online:
2021-05-01
Contact:
Qihong Fang
About author:
*E-mail address: fangqh1327@hnu.edu.cn (Q. Fang).Jia Li, Baobin Xie, Qihong Fang, Bin Liu, Yong Liu, Peter K. Liaw. High-throughput simulation combined machine learning search for optimum elemental composition in medium entropy alloy[J]. J. Mater. Sci. Technol., 2021, 68: 70-75.
Fig. 4. The predicted ultimate tensile strength values from the machine-learning model as a function of the simulated ultimate tensile strength values for (a) the training set, (b) the validation set, (c) testing data set and (d) all data (The dotted line represents the output data of the model in exactly the same as the target data in the data set; the solid line represents the regression results between the output data and the target data).
Fig. 5. The mean squared error (MSE) between predicted and simulated values from the “chemical composition - UTS” model as a function for the training set, the validation set and the testing set with the increase of training epochs. It can be seen that the epoch and MSE of the stopping point (the point with best performance) are 2 and 0.067, respectively.
System | MD result (GPa) | Machine learning result (GPa) | Error (%) |
---|---|---|---|
Co21Cr20Ni59 | 23.5 | 23.9 | 1.7 |
Co29Cr30Ni41 | 26.7 | 26.9 | 0.7 |
Co49Cr30Ni21 | 29.4 | 29.2 | 0.7 |
Table 1 The strength and error in different compositional regions.
System | MD result (GPa) | Machine learning result (GPa) | Error (%) |
---|---|---|---|
Co21Cr20Ni59 | 23.5 | 23.9 | 1.7 |
Co29Cr30Ni41 | 26.7 | 26.9 | 0.7 |
Co49Cr30Ni21 | 29.4 | 29.2 | 0.7 |
Element | Density (g/cm3) | Price ($/kg) |
---|---|---|
Co | 8.9 | 3.88 |
Cr | 7.2 | 0.86 |
Ni | 8.9 | 1.58 |
Table 2 The density and price of pure element.
Element | Density (g/cm3) | Price ($/kg) |
---|---|---|
Co | 8.9 | 3.88 |
Cr | 7.2 | 0.86 |
Ni | 8.9 | 1.58 |
[1] |
B. Gludovatz, A. Hohenwarter, D. Catoor, E.H. Chang, E.P. George, R.O. Ritchie, Science, 345 (2014), pp. 1153-1158.
DOI URL |
[2] |
Z.F. Lei, X.J. Liu, Y. Wu, H. Wang, S.H. Jiang, S.D. Wang, X.D. Hui, Y.D. Wu, B. Gault, P. Kontis, D. Raabe, L. Gu, Q.H. Zhang, H.W. Chen, H.T. Wang, J.B. Liu, K. An, Q.S. Zeng, T.G. Nieh, Z.P. Lu, Nature, 563 (2018), p. 546.
DOI URL |
[3] |
T. Yang, Y.L. Zhao, Y. Tong, Z.B. Jiao, J. Wei, J.X. Cai, K. Lu, Y. Liu, C.T. Liu, Science, 362 (2018), pp. 933-937.
DOI URL |
[4] |
Z. Li, K.G. Pradeep, Y. Deng, D. Raabe, C.C. Tasan, Nature, 534 (2016), pp. 227-230.
DOI URL |
[5] |
Y. Zhang, T.T. Zuo, Z. Tang, M.C. Gao, K.A. Dahmen, P.K. Liaw, Z.P. Lu, Prog. Mater. Sci., 61 (2014), pp. 1-93.
DOI URL |
[6] |
O.N. Senkov, D.B. Miracle, Acta Mater., 122 (2015), pp. 448-511.
DOI URL |
[7] |
P. Shi, W. Ren, T. Zheng, Z. Ren, X. Hou, J. Peng, P.K. Liaw, Nat. Commun., 10 (2019), p. 489.
DOI URL |
[8] |
C. Lee, G. Song, M.C. Gao, R. Feng, P. Chen, J. Brechtl, Y. Chen, K. An, W. Guo, J.D. Poplawsky, S. Li, A.T. Samaei, W. Chen, A. Hu, H. Choo, P.K. Liaw, Acta Mater., 160 (2018), pp. 158-172.
DOI URL |
[9] |
Y.Z. Shi, L. Collins, N. Balke, P.K. Liaw, B. Yang, Appl. Surf. Sci., 493 (2018), pp. 533-544.
DOI URL |
[10] |
Y.Z. Shi, L. Collins, R. Feng, C. Zhang, N. Balke, P.K. Liaw, B. Yang, Corros. Sci., 133 (2018), pp. 120-131.
DOI URL |
[11] |
Y.Z. Shi, B. Yang, X. Xie, J. Brechtl, K.A. Dahmen, P.K. Liaw, Corros. Sci., 119 (2017), pp. 33-45.
DOI URL |
[12] |
J. Li, H.T. Chen, S. Li, Q. Fang, Y. Liu, L. Liang, H. Wu, P.K. Liaw, Mater. Sci. Eng. A, 760 (2019), pp. 359-365.
DOI URL |
[13] |
T.M. Smith, B.D. Esser, N. Antolin, A. Carlsson, R.E.A. Williams, Nat. Commun., 7 (2016), p. 13434.
DOI PMID |
[14] |
M. Calcagnotto, D. Ponge, D. Raabe, Metall. Mater. Trans. A, 43 (2012), pp. 37-46.
DOI URL |
[15] |
J. Hu, Y.N. Shi, X. Sauvage, G. Sha, K. Lu, Science, 355 (2017), pp. 1292-1296.
DOI URL |
[16] |
M.J. Harvey, G.D. Fabritiis, Drug Discov. Today, 17 (2012), pp. 1059-1062.
DOI URL |
[17] |
S. Doerr, M.J. Harvey, F. Noe, G.D. Fabritiis, J. Chem. Theory Comput., 12 (2016), pp. 1845-1852.
DOI PMID |
[18] | I. Buch, M.J. Harvey, T. Giorgino, D.P. Anderson, G.D. Fabritiis, J. Chem. Inf. Model., 550 (2010), pp. 397-403. |
[19] |
G. Hautier, A. Jain, H.L. Chen, C. Moore, S.P. Ong, G. Ceder, J. Mater. Chem., 21 (2011), pp. 17147-17153.
DOI URL |
[20] |
A. Jain, G. Hautier, C.J. Moore, Comput. Mater. Sci., 50 (2011), pp. 2295-2310.
DOI URL |
[21] |
J. Schmidhuber, Neural Netw., 61 (2015), pp. 85-117.
PMID |
[22] | Y. Zhang, S. Yang, J. Evans Acta Mater., 56 (2008), pp. 1094-1105. |
[23] |
M. Rupp, A. Tkatchenko, K.R. Müller, Phys. Rev. Lett., 108 (2012), 058301.
DOI URL |
[24] |
B. Meredig, C. Wolverton, Nat. Mater., 12 (2013), p. 123.
DOI PMID |
[25] |
L. Ward, A. Agrawal, A. Choudhary, C. Wolverton, NPJ Comput. Mater., 2 (2016), p. 16028.
DOI URL |
[26] |
Y. Lin, J. Zhang, J. Zhong, Comput. Mater. Sci., 43 (2008), pp. 752-758.
DOI URL |
[27] |
N.S. Reddy, J. Krishnaiah, H.B. Young, J.S. Lee, Comput. Mater. Sci., 101 (2015), pp. 120-126.
DOI URL |
[28] |
M.S. Ozerdem, S. Kolukisa, Mater. Des., 30 (2009), pp. 764-769.
DOI URL |
[29] | W.M. Choi, Y.H. Jo, S.S. Sohn, S. Lee, B.J. Lee, NPJ Comput. Mater., 8 (2018), pp. 1-9. |
[30] |
V. Nourani, H. Gökçekuş, I.K. Umar, Environ. Res., 180 (2020), 108852.
DOI URL |
[31] | A.K. Jain, K. Mao, K.M. Mohiuddin, Computer, 29 (1996), pp. 31-44. |
[32] | J. Li, J.H. Cheng, J.Y. Shi, F. Huang, Advances in Computer Science and Information Engineering Springer (2012), pp. 553-558. |
[33] |
M. Igor, C. Jan, K. Zuzana, N. Jitka, K. Michael, N. Erich, K. Ivo, H. Vit, D. Ivo, Mater. Sci. Eng. A, 701 (2017), pp. 370-380.
DOI URL |
[34] |
B. Wang, H.Y. He, M. Naeem, S. Lan, S. Harjo, T. Kawasaki, Scr. Mater., 155 (2018), pp. 54-57.
DOI URL |
[35] |
L. Wei, Y. Liu, Q. Li, Y.F. Cheng, Corros. Sci., 146 (2019), pp. 44-57.
DOI URL |
[36] |
W. Xu, N. Birbilis, G. Sha, Y. Wang, J.E. Daniels, Y. Xiao, M. Ferry, Nat. Mater., 14 (2015), pp. 1229-1235.
DOI URL |
[37] | M.C. Troparevsky, J.R. Morris, P.R. Kent, A.R. Lupini, G.M. Stocks, Phys. Rev. X, 5 (2015), 011041. |
[38] |
C. Haase, F. Tang, M.B. Wilms, A. Weisheit, B. Hallstedt, Mater. Sci. Eng. A, 688 (2017), pp. 180-189.
DOI URL |
[39] |
Y. Lederer, C. Toher, K.S. Vecchio, S. Curtarolo, Acta Mater., 159 (2018), pp. 364-383.
DOI URL |
[1] | Tao Zheng, Xiaobing Hu, Feng He, Qingfeng Wu, Bin Han, Chen Da, Junjie Li, Zhijun Wang, Jincheng Wang, Ji-jung Kai, Zhenhai Xia, C.T. Liu. Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging [J]. J. Mater. Sci. Technol., 2021, 69(0): 156-167. |
[2] | Ruobin Chang, Wei Fang, Jiaohui Yan, Haoyang Yu, Xi Bai, Jia Li, Shiying Wang, Shijian Zheng, Fuxing Yin. Microstructure and mechanical properties of CoCrNi-Mo medium entropy alloys: Experiments and first-principle calculations [J]. J. Mater. Sci. Technol., 2021, 62(0): 25-33. |
[3] | Fu-Zhi Dai, Yinjie Sun, Bo Wen, Huimin Xiang, Yanchun Zhou. Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential [J]. J. Mater. Sci. Technol., 2021, 72(0): 8-15. |
[4] | Yujie Chen, Yan Fang, Xiaoqian Fu, Yiping Lu, Sijing Chen, Hongbin Bei, Qian Yu. Origin of strong solid solution strengthening in the CrCoNi-W medium entropy alloy [J]. J. Mater. Sci. Technol., 2021, 73(0): 101-107. |
[5] | Jia Li, Li Li, Chao Jiang, Qihong Fang, Feng Liu, Yong Liu, Peter K. Liaw. Probing deformation mechanisms of gradient nanostructured CrCoNi medium entropy alloy [J]. J. Mater. Sci. Technol., 2020, 57(0): 85-91. |
[6] | Yingli Liu, Chen Niu, Zhuo Wang, Yong Gan, Yan Zhu, Shuhong Sun, Tao Shen. Machine learning in materials genome initiative: A review [J]. J. Mater. Sci. Technol., 2020, 57(0): 113-122. |
[7] | Fu-Zhi Dai, Bo Wen, Yinjie Sun, Huimin Xiang, Yanchun Zhou. Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential [J]. J. Mater. Sci. Technol., 2020, 43(0): 168-174. |
[8] | S.J. Tsianikas, Y. Chen, Z. Xie. Deciphering deformation mechanisms of hierarchical dual-phase CrCoNi coatings [J]. J. Mater. Sci. Technol., 2020, 39(0): 7-13. |
[9] | S.J. Tsianikas, Y. Chen, Z. Xie. Deciphering deformation mechanisms of hierarchical dual-phase CrCoNi coatings [J]. J. Mater. Sci. Technol., 2020, 39(0): 183-189. |
[10] | Bin Gan, Jeffrey M. Wheeler, Zhongnan Bi, Lin Liu, Jun Zhang, Hengzhi Fu. Superb cryogenic strength of equiatomic CrCoNi derived from gradient hierarchical microstructure [J]. J. Mater. Sci. Technol., 2019, 35(6): 957-961. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||