机器学习辅助合金理性设计研究进展

付华栋; 张洪涛; 王长胜; 雍维; 谢建新

doi:10.1007/s12613-022-2458-8

Volume 29 Issue 4

Apr. 2022

Turn off MathJax

图(6) / 表(1)

数据统计

计量

文章访问数: 3563
HTML全文浏览量: 337
PDF下载量: 415
被引次数: 0

文章导航 > 矿物冶金与材料学报（英文） > 2022 > 29(4): 635-644

Huadong Fu, Hongtao Zhang, Changsheng Wang, Wei Yong, and Jianxin Xie, Recent progress in the machine learning-assisted rational design of alloys, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 635-644. https://doi.org/10.1007/s12613-022-2458-8

Cite this article as:

Huadong Fu, Hongtao Zhang, Changsheng Wang, Wei Yong, and Jianxin Xie, Recent progress in the machine learning-assisted rational design of alloys, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 635-644. https://doi.org/10.1007/s12613-022-2458-8

Citation:

PDF( 1202 KB)

引用本文 PDF XML SpringerLink

特约综述

机器学习辅助合金理性设计研究进展

1)
北京科技大学北京材料基因工程高精尖创新中心, 北京 100083, 中国
2)
北京科技大学材料先进制备技术教育部重点实验室, 北京 100083, 中国
3)
北京科技大学现代交通金属材料与加工技术北京实验室, 北京 100083, 中国

通讯作者:
谢建新 E-mail: jxxie@mater.ustb.edu.cn

文章亮点

(1) 介绍了机器学习辅助金属材料理性设计的基本策略。

(2) 综述了金属材料成分和工艺的逆向设计、选择设计和优化设计方法。

(3) 展望了机器学习辅助金属材料理性设计的未来发展趋势。

中文摘要

基于经验的传统“试错法”金属材料设计存在试错周期长、成本高等问题，大数据和人工智能技术的快速发展，为金属材料高效研发提供了新的途径—机器学习模型预测辅助材料设计。本文介绍了机器学习辅助金属材料理性设计的基本策略，重点综述了面向性能需求的合金成分逆向设计、基于合金元素物理化学特征或材料组织结构特征建模的合金成分选择设计、基于迭代反馈优化的合金成分和工艺参数优化设计三方面的研究进展，展望了机器学习辅助金属材料理性设计的未来发展趋势。

关键词:

Invited Review

Recent progress in the machine learning-assisted rational design of alloys

+ Author Affiliations

Abstract

Abstract

Alloys designed with the traditional trial and error method have encountered several problems, such as long trial cycles and high costs. The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials, that is, machine learning-assisted design. In this paper, the basic strategy for the machine learning-assisted rational design of alloys was introduced. Research progress in the property-oriented reversal design of alloy composition, the screening design of alloy composition based on models established using element physical and chemical features or microstructure factors, and the optimal design of alloy composition and process parameters based on iterative feedback optimization was reviewed. Results showed the great advantages of machine learning, including high efficiency and low cost. Future development trends for the machine learning-assisted rational design of alloys were also discussed. Interpretable modeling, integrated modeling, high-throughput combination, multi-objective optimization, and innovative platform building were suggested as fields of great interest.
- machine learning;
- data mining;
- rational design;
- alloys

FullText(HTML)

Supplements(0)

References(67)

References

[1]	P.H. Abelson, Materials research and applications, Science, 274(1996), No. 5291, p. 1283. doi: 10.1126/science.274.5291.1283
[2]	K. Lu, The future of metals, Science, 328(2010), p. 319. doi: 10.1126/science.1185866
[3]	U. Bhandari, C.Y. Zhang, S.M. Guo, and S.Z. Yang, First-principles study on the mechanical and thermodynamic properties of MoNbTaTiW, Int. J. Miner. Metall. Mater., 27(2020), No. 10, p. 1398. doi: 10.1007/s12613-020-2077-1
[4]	Z.S. Nong, H.Y. Wang, and J. C. Zhu, First-principles calculations of structural, elastic and electronic properties of (TaNb)_0.67 (HfZrTi)_0.33 high-entropy alloy under high pressure, Int. J. Miner. Metall. Mater., 27(2020), No. 10, p. 1405. doi: 10.1007/s12613-020-2095-z
[5]	J.P. Immarigeon, R.T. Holt, A.K. Koul, L. Zhao, W. Wallace, and J.C. Beddoes, Lightweight materials for aircraft applications, Mater. Charact., 35(1995), No. 1, p. 41. doi: 10.1016/1044-5803(95)00066-6
[6]	T.M. Pollock, Alloy design for aircraft engines, Nat. Mater., 15(2016), No. 8, p. 809. doi: 10.1038/nmat4709
[7]	Z. Li, Z. Xiao, Y.B. Jiang, Q. Lei, and J.X. Xie, Composition design, phase transition and fabrication of copper alloys with high strength and electrical conductivity, Chin. J. Noferrous Met., 29(2019), No. 9, p. 2009.
[8]	Y.J. Su, D.W. Zhang, Q. Feng, and J.X. Xie, A vision of materials genome engineering in China, Engineering, 2022. DOI: 10.1016/j.eng.2021.12.008
[9]	J.X. Xie, Y.J. Su, D.Z. Xue, X. Jiang, H.D. Fu, and H.Y. Huang, Machine learning for materials research and development, Acta Metall. Sinica, 57(2021), No. 11, p. 1343.
[10]	Z.H. Deng, H.Q. Yin, X. Jiang, C. Zhang, G.F. Zhang, B. Xu, G.Q. Yang, T. Zhang, M. Wu, and X.H. Qu, Machine-learning-assisted prediction of the mechanical properties of Cu–Al alloy, Int. J. Miner. Metall. Mater., 27(2020), No. 3, p. 362. doi: 10.1007/s12613-019-1894-6
[11]	K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, and A. Walsh, Machine learning for molecular and materials science, Nature, 559(2018), No. 7715, p. 547. doi: 10.1038/s41586-018-0337-2
[12]	T. Lookman, P.V. Balachandran, D.Z. Xue, and R.H. Yuan, Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design, npj Comput. Mater., 5(2019), art. No. 21. doi: 10.1038/s41524-019-0153-8
[13]	J. Wang, X.Y. Yang, Z. Zeng, X.L. Zhang, X.S. Zhao, and Z.G. Wang, New methods for prediction of elastic constants based on density functional theory combined with machine learning, Comput. Mater. Sci., 138(2017), p. 135. doi: 10.1016/j.commatsci.2017.06.015
[14]	L. Huber, R. Hadian, B. Grabowski, and J. Neugebauer, A machine learning approach to model solute grain boundary segregation, npj Comput. Mater., 4(2018), art. No. 64. doi: 10.1038/s41524-018-0122-7
[15]	A. Agrawal and A. Choudhary, An online tool for predicting fatigue strength of steel alloys based on ensemble data mining, Int. J. Fatigue, 113(2018), p. 389. doi: 10.1016/j.ijfatigue.2018.04.017
[16]	W.J. Huang, P. Martin, and H.L. Zhuang, Machine-learning phase prediction of high-entropy alloys, Acta Mater., 169(2019), p. 225. doi: 10.1016/j.actamat.2019.03.012
[17]	Y.F. Chen, Y. Tian, Y.M. Zhou, D.Q. Fang, X.D. Ding, J. Sun, and D.Z. Xue, Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy, J. Alloys Compd., 844(2020), art. No. 156159. doi: 10.1016/j.jallcom.2020.156159
[18]	A. Famili, W.M. Shen, R. Weber, and E. Simoudis, Data preprocessing and intelligent data analysis, Intell. Data Anal., 1(1997), No. 1-4, p. 3. doi: 10.1016/S1088-467X(98)00007-9
[19]	P. Raccuglia, K.C. Elbert, P.D.F. Adler, C. Falk, M.B. Wenny, A. Mollo, M. Zeller, S.A. Friedler, J. Schrier, and A.J. Norquist, Machine-learning-assisted materials discovery using failed experiments, Nature, 533(2016), No. 7601, p. 73. doi: 10.1038/nature17439
[20]	R.H. Yuan, Z. Liu, P.V. Balachandran, D.Q. Xue, Y.M. Zhou, X.D. Ding, J. Sun, D.Z. Xue, and T. Lookman, Accelerated discovery of large electrostrains in BaTiO₃-based piezoelectrics using active learning, Adv. Mater., 30(2018), No. 7, art. No. 1702884. doi: 10.1002/adma.201702884
[21]	D.H. Wolpert and W.G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1(1997), No. 1, p. 67. doi: 10.1109/4235.585893
[22]	J.R. Quinlan, Induction of decision trees, Mach. Learn., 1(1986), p. 81. doi: 10.1007/BF00116251
[23]	D.E. Rumelhart, G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors, Nature, 323(1986), No. 6088, p. 533. doi: 10.1038/323533a0
[24]	C. Cortes and V. Vapnik, Support-vector networks, Mach. Learn., 20(1995), p. 273. doi: 10.1007/BF00994018
[25]	I. Kononenko, Semi-naive Bayesian classifier, [in] European Working Session on Learning, Springer, Berlin, Heidelberg, 1991, p. 206.
[26]	A. Zunger, Inverse design in search of materials with target functionalities, Nat. Rev. Chem., 2(2018), art. No. 0121. doi: 10.1038/s41570-018-0121
[27]	S. Kunnikuruvan, A. Chakraborty, and D.T. Major, Monte Carlo- and simulated-annealing-based funneled approach for the prediction of cation ordering in mixed transition-metal oxide materials, J. Phys. Chem. C, 124(2020), No. 50, p. 27366. doi: 10.1021/acs.jpcc.0c08579
[28]	S.B. Roshan, M.B. Jooibari, R. Teimouri, G. Asgharzadeh-Ahmadi, M. Falahati-Naghibi, and H. Sohrabpoor, Optimization of friction stir welding process of AA7075 aluminum alloy to achieve desirable mechanical properties using ANFIS models and simulated annealing algorithm, Int. J. Adv. Manuf. Technol., 69(2013), No. 5-8, p. 1803. doi: 10.1007/s00170-013-5131-6
[29]	N. Chakraborti, R. Sreevathsan, R. Jayakanth, and B. Bhattacharya, Tailor-made material design: An evolutionary approach using multi-objective genetic algorithms, Comput. Mater. Sci., 45(2009), No. 1, p. 1. doi: 10.1016/j.commatsci.2008.03.057
[30]	R. Sreevathsan, B. Bhattacharya, and N. Chakraborti, Designing ionic materials through multiobjective genetic algorithms, Mater. Manuf. Processes, 24(2009), No. 2, p. 162.
[31]	T.D. Liu, L.Y. Xu, G.F. Shao, N.N. Tu, J.P. Tao, and Y.H. Wen, Structural optimization of Pt–Pd–Rh trimetallic nanoparticles using improved genetic algorithm, J. Alloys Compd., 663(2016), p. 466. doi: 10.1016/j.jallcom.2015.12.146
[32]	C.S. Wang, H.D. Fu, L. Jiang, D.Z. Xue, and J.X. Xie, A property-oriented design strategy for high performance copper alloys via machine learning, npj Comput. Mater., 5(2019), art. No. 87. doi: 10.1038/s41524-019-0227-7
[33]	Y.J. Su, H.D. Fu, Y. Bai, X. Jiang, and J.X. Xie, Progress in materials genome engineering in China, Acta Metall. Sin., 56(2020), No. 10, p. 1313.
[34]	L. Jiang, C.S. Wang, H.D. Fu, J. Shen, Z.H. Zhang, and J.X. Xie, Discovery of aluminum alloys with ultra-strength and high-toughness via a property-oriented design strategy, J. Mater. Sci. Technol., 98(2022), p. 33. doi: 10.1016/j.jmst.2021.05.011
[35]	H.X. Zong, G. Pilania, X.D. Ding, G.J. Ackland, and T. Lookman, Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning, npj Comput. Mater., 4(2018), art. No. 48. doi: 10.1038/s41524-018-0103-x
[36]	K. Tsutsui, H. Terasaki, T. Maemura, K. Hayashi, K. Moriguchi, and S. Morito, Microstructural diagram for steel based on crystallography with machine learning, Comput. Mater. Sci., 159(2019), p. 403. doi: 10.1016/j.commatsci.2018.12.003
[37]	S. Feng, H.Y. Zhou, and H.B. Dong, Using deep neural network with small dataset to predict material defects, Mater. Des., 162(2019), p. 300. doi: 10.1016/j.matdes.2018.11.060
[38]	L. Ward, A. Agrawal, A. Choudhary, and C. Wolverton, A general-purpose machine learning framework for predicting properties of inorganic materials, npj Comput. Mater., 2(2016), art. No. 16028. doi: 10.1038/npjcompumats.2016.28
[39]	L. Ward, S.C. O’Keeffe, J. Stevick, G.R. Jelbert, M. Aykol, and C. Wolverton, A machine learning approach for engineering bulk metallic glass alloys, Acta Mater., 159(2018), p. 102. doi: 10.1016/j.actamat.2018.08.002
[40]	N. Islam, W.J. Huang, and H.L. Zhuang, Machine learning for phase selection in multi-principal element alloys, Comput. Mater. Sci., 150(2018), p. 230. doi: 10.1016/j.commatsci.2018.04.003
[41]	P. Villars and L.D. Calvert, Pearson’s Handbook of Crystallographic Data for Intermetallic Phases, ASM Int., Ohio, 1991.
[42]	R.H. Yuan, D.Q. Xue, D.Z. Xue, J.S. Li, X.D. Ding, J. Sun, and T. Lookman, Knowledge-based descriptor for the compositional dependence of the phase transition in BaTiO₃-based ferroelectrics, ACS Appl. Mater. Interfaces, 12(2020), No. 40, p. 44970. doi: 10.1021/acsami.0c12763
[43]	S.Z. Li, H.R. Zhang, D.B. Dai, G.T. Ding, X. Wei, and Y.K. Guo, Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning, J. Alloys Compd., 782(2019), p. 110. doi: 10.1016/j.jallcom.2018.12.136
[44]	Z.C. Lu, X. Chen, X.J. Liu, D.Y. Lin, Y. Wu, Y.B. Zhang, H. Wang, S.H. Jiang, H.X. Li, X.Z. Wang, and Z.P. Lu, Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses, npj Comput. Mater., 6(2020), art. No. 187. doi: 10.1038/s41524-020-00460-x
[45]	K. Kaufmann and K.S. Vecchio, Searching for high entropy alloys: A machine learning approach, Acta Mater., 198(2020), p. 178. doi: 10.1016/j.actamat.2020.07.065
[46]	V. Stanev, C. Oses, A.G. Kusne, E. Rodriguez, J. Paglione, S. Curtarolo, and I. Takeuchi, Machine learning modeling of superconducting critical temperature, npj Comput. Mater., 4(2018), art. No. 29. doi: 10.1038/s41524-018-0085-8
[47]	H. Wu, A. Lorenson, B. Anderson, L. Witteman, H.T. Wu, B. Meredig, and D. Morgan, Robust FCC solute diffusion predictions from ab-initio machine learning methods, Comput. Mater. Sci., 134(2017), p. 160. doi: 10.1016/j.commatsci.2017.03.052
[48]	S. Kirklin, J.E. Saal, V.I. Hegde, and C. Wolverton, High-throughput computational search for strengthening precipitates in alloys, Acta Mater., 102(2016), p. 125. doi: 10.1016/j.actamat.2015.09.016
[49]	Y. Zhang, C. Wen, C.X. Wang, S. Antonov, D.Z. Xue, Y. Bai, and Y.J. Su, Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models, Acta Mater., 185(2020), p. 528. doi: 10.1016/j.actamat.2019.11.067
[50]	Z.Q. Zhou, Y.J. Zhou, Q.F. He, Z.Y. Ding, F.C. Li, and Y. Yang, Machine learning guided appraisal and exploration of phase design for high entropy alloys, npj Comput. Mater., 5(2019), art. No. 128. doi: 10.1038/s41524-019-0265-1
[51]	J.M. Rickman, H.M. Chan, M.P. Harmer, J.A. Smeltzer, C.J. Marvel, A. Roy, and G. Balasubramanian, Materials informatics for the screening of multi-principal elements and high-entropy alloys, Nat. Commun., 10(2019), art. No. 2618. doi: 10.1038/s41467-019-10533-1
[52]	X.M. Wang, Y.L. Xu, J. Yang, J.Y. Ni, W. Zhang, and W.H. Zhu, ThermoEPred-EL: Robust bandgap predictions of chalcogenides with diamond-like structure via feature cross-based stacked ensemble learning, Comput. Mater. Sci., 169(2019), art. No. 109117. doi: 10.1016/j.commatsci.2019.109117
[53]	D.B. Dai, T. Xu, X. Wei, G.T. Ding, Y. Xu, J.C. Zhang, and H.R. Zhang, Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys, Comput. Mater. Sci., 175(2020), art. No. 109618. doi: 10.1016/j.commatsci.2020.109618
[54]	J. Benesty, J.D. Chen, Y.T. Huang, and I. Cohen, Noise Reduction in Speech Processing, Springer, Berlin, Heidelberg, 2009.
[55]	R. Tibshirani, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B, 58(1996), No. 1, p. 267.
[56]	P.M. Granitto, C. Furlanello, F. Biasioli, and F. Gasperi, Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products, Chemom. Intell. Lab. Syst., 83(2006), No. 2, p. 83. doi: 10.1016/j.chemolab.2006.01.007
[57]	I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Mach. Learn., 46(2002), p. 389. doi: 10.1023/A:1012487302797
[58]	Y. Liu, J.M. Wu, M. Avdeev, and S.Q. Shi, Multi-layer feature selection incorporating weighted score-based expert knowledge toward modeling materials with targeted properties, Adv. Theor. Simul., 3(2020), No. 2, art. No. 1900215. doi: 10.1002/adts.201900215
[59]	H.T. Zhang, H.D. Fu, X.Q. He, C.S. Wang, L. Jiang, L.Q. Chen, and J.X. Xie, Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening, Acta Mater., 200(2020), p. 803. doi: 10.1016/j.actamat.2020.09.068
[60]	J.W. Yeh, Recent progress in high-entropy alloys, Eur. J. Control, 31(2006), No. 6, p. 633.
[61]	C. Wen, C.X. Wang, Y. Zhang, S. Antonov, D.Z. Xue, T. Lookman, and Y.J. Su, Modeling solid solution strengthening in high entropy alloys using machine learning, Acta Mater., 212(2021), art. No. 116917. doi: 10.1016/j.actamat.2021.116917
[62]	H.T. Zhang, H.D. Fu, S.C. Zhu, W. Yong, and J.X. Xie, Machine learning assisted composition effective design for precipitation strengthened copper alloys, Acta Mater., 215(2021), art. No. 117118. doi: 10.1016/j.actamat.2021.117118
[63]	D.Z. Xue, P.V. Balachandran, J. Hogden, J. Theiler, D.Q. Xue, and T. Lookman, Accelerated search for materials with targeted properties by adaptive design, Nat. Commun., 7(2016), art. No. 11241. doi: 10.1038/ncomms11241
[64]	D.Z. Xue, D.Q. Xue, R.H. Yuan, Y.M. Zhou, P.V. Balachandran, X.D. Ding, J. Sun, and T. Lookman, An informatics approach to transformation temperatures of NiTi-based shape memory alloys, Acta Mater., 125(2017), p. 532. doi: 10.1016/j.actamat.2016.12.009
[65]	C. Wen, Y. Zhang, C.X. Wang, D.Z. Xue, Y. Bai, S. Antonov, L.H. Dai, T. Lookman, and Y.J. Su, Machine learning assisted design of high entropy alloys with desired property, Acta Mater., 170(2019), p. 109. doi: 10.1016/j.actamat.2019.03.010
[66]	Y.W. Liu, L.Y. Wang, H. Zhang, G.M. Zhu, J. Wang, Y.H. Zhang, and X.Q. Zeng, Accelerated development of high-strength magnesium alloys by machine learning, Metall. Mater. Trans. A, 52(2021), No. 3, p. 943. doi: 10.1007/s11661-020-06132-1
[67]	H.T. Zhang, H.D. Fu, Y.H. Shen, and J.X. Xie, Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu–Ni–Co–Si–X alloy via Bayesian optimization machine learning, Int. J. Miner. Metall. Mater., 2022. DOI: 10.1007/s12613-022-2479-3