留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码
Volume 30 Issue 6
Jun.  2023
Turn off MathJax
目录

图(12)

数据统计

分享

计量
  • 文章访问数:  4552
  • HTML全文浏览量:  1290
  • PDF下载量:  492
  • 被引次数: 0
文章导航 >  矿物冶金与材料学报（英文）  > 2023  >  30(6): 1003-1024
Guangfei Pan, Feiyang Wang, Chunlei Shang, Honghui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao, Xiaoye Zhou, and Xinping Mao, Advances in machine learning- and artificial intelligence-assisted material design of steels, Int. J. Miner. Metall. Mater., 30(2023), No. 6, pp. 1003-1024. https://doi.org/10.1007/s12613-022-2595-0
Cite this article as:
Guangfei Pan, Feiyang Wang, Chunlei Shang, Honghui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao, Xiaoye Zhou, and Xinping Mao, Advances in machine learning- and artificial intelligence-assisted material design of steels, Int. J. Miner. Metall. Mater., 30(2023), No. 6, pp. 1003-1024. https://doi.org/10.1007/s12613-022-2595-0
shu
引用本文 PDF XML SpringerLink
特约综述

机器学习和人工智能辅助钢铁材料设计的研究进展

  • 1)

    北京科技大学材料基因组工程北京市高精尖创新中心, 北京 100083, 中国

  • 2)

    广东省材料科学与技术实验室阳江分部(阳江先进合金实验室), 阳江 529500, 中国

  • 3)

    广东省海洋土木工程耐久性重点实验室, 深圳大学土木工程学院, 深圳 518060, 中国

  • 通讯作者:

    吴宏辉    E-mail: wuhonghui@ustb.edu.cn

    汪水泽    E-mail: wangshuize@ustb.edu.cn

文章亮点

  • (1) 以材料四面体为导向，系统地综述了机器学习方法在钢铁材料“成分-工艺-组织-性能”研究领域的应用。
  • (2) 机器学习算法的快速发展将显著提升对结构材料构效关系的深入理解。
  • (3) 基于计算材料学、迁移学习和数据挖掘等方法以扩展数据集，是未来机器学习和人工智能辅助钢材设计的重要发展方向。
  • 随着人工智能技术的快速发展和材料数据的显著增加，机器学习和人工智能辅助设计高性能钢材正成为材料科学的主流范式。机器学习方法是一种基于计算机科学、统计学及材料科学之间的跨学科科学，聚焦于发现众多数据之间的相关性。与材料科学中传统的物理建模方法相比，机器学习方法的主要优势在于克服了材料本身复杂的物理机制，为新型高性能材料的研发提供了新的思路。本文从数据预处理和机器学习模型的介绍开始，包括算法选择和模型评估。然后，以优化成分、结构、工艺和性能为主题，回顾了机器学习方法在钢铁研究领域应用的一些典型案例。此外，还介绍了机器学习方法在以性能为导向的材料成分逆向设计工程以及在钢材缺陷检测领域中的应用。最后，探讨了机器学习在材料领域的适用性和局限性，并对未来的发展方向和前景进行了展望。
    • Invited Review

    Advances in machine learning- and artificial intelligence-assisted material design of steels

    + Author Affiliations
      Abstract
    • With the rapid development of artificial intelligence technology and increasing material data, machine learning- and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science. Machine learning methods, based on an interdisciplinary discipline between computer science, statistics and material science, are good at discovering correlations between numerous data points. Compared with the traditional physical modeling method in material science, the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials. This review starts with data preprocessing and the introduction of different machine learning models, including algorithm selection and model evaluation. Then, some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition, structure, processing, and performance. The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed. Finally, the applicability and limitations of machine learning in the material field are summarized, and future directions and prospects are discussed.
      • FullText(HTML)
    • loading
      • Supplements(0)
      • References(93)
    • [1]
      G.B. Olson, Genomic materials design: The ferrous frontier, Acta Mater., 61(2013), No. 3, p. 771. doi: 10.1016/j.actamat.2012.10.045
      [2]
      G.L.W. Hart, T. Mueller, C. Toher, and S. Curtarolo, Machine learning for alloys, Nat. Rev. Mater., 6(2021), No. 8, p. 730. doi: 10.1038/s41578-021-00340-w
      [3]
      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
      [4]
      C. Chen, Y.X. Zuo, W.K. Ye, X.G. Li, Z. Deng, and S.P. Ong, A critical review of machine learning of energy materials, Adv. Energy Mater., 10(2020), No. 8, art. No. 1903242. doi: 10.1002/aenm.201903242
      [5]
      R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, and C. Kim, Machine learning in materials informatics: Recent applications and prospects, Npj Comput. Mater., 3(2017), art. No. 54. doi: 10.1038/s41524-017-0056-5
      [6]
      R. Batra, L. Song, and R. Ramprasad, Emerging materials intelligence ecosystems propelled by machine learning, Nat. Rev. Mater., 6(2020), No. 8, p. 655. doi: 10.1038/s41578-020-00255-y
      [7]
      J.M. Rickman, T. Lookman, and S.V. Kalinin, Materials informatics: From the atomic-level to the continuum, Acta Mater., 168(2019), p. 473. doi: 10.1016/j.actamat.2019.01.051
      [8]
      S. Feng, H.D. Fu, H.Y. Zhou, Y. Wu, Z.P. Lu, and H.B. Dong, A general and transferable deep learning framework for predicting phase formation in materials, Npj Comput. Mater., 7(2021), art. No. 10. doi: 10.1038/s41524-020-00488-z
      [9]
      S. Chakraborty, P.P. Chattopadhyay, S.K. Ghosh, and S. Datta, Incorporation of prior knowledge in neural network model for continuous cooling of steel using genetic algorithm, Appl. Soft Comput., 58(2017), p. 297. doi: 10.1016/j.asoc.2017.05.001
      [10]
      J. Schmidt, M.R.G. Marques, S. Botti, and M.A.L. Marques, Recent advances and applications of machine learning in solid-state materials science, Npj Comput. Mater., 5(2019), art. No. 83. doi: 10.1038/s41524-019-0221-0
      [11]
      N. Nosengo and G. Ceder, Can artificial intelligence create the next wonder material, Nature, 533(2016), No. 7601, p. 22. doi: 10.1038/533022a
      [12]
      X.Y. Zhou, J.H. Zhu, Y. Wu, X.S. Yang, T. Lookman, and H.H. Wu, Machine learning assisted design of FeCoNiCrMn high-entropy alloys with ultra-low hydrogen diffusion coefficients, Acta Mater., 224(2022), art. No. 117535. doi: 10.1016/j.actamat.2021.117535
      [13]
      Z.L. Song, X.W. Chen, F.B. Meng, et al., Machine learning in materials design: Algorithm and application, Chin. Phys. B, 29(2020), No. 11, art. No. 116103. doi: 10.1088/1674-1056/abc0e3
      [14]
      Y.L. Liu, C. Niu, Z. Wang, et al., Machine learning in materials genome initiative: A review, J. Mater. Sci. Technol., 57(2020), p. 113. doi: 10.1016/j.jmst.2020.01.067
      [15]
      Y.M. Chen, S.Z. Wang, J. Xiong, et al., Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning, J. Mater. Sci. Technol., 132(2023), p. 213. doi: 10.1016/j.jmst.2022.05.051
      [16]
      K. Tsutsui, H. Terasaki, K. Uto, et al., A methodology of steel microstructure recognition using SEM images by machine learning based on textural analysis, Mater. Today Commun., 25(2020), art. No. 101514. doi: 10.1016/j.mtcomm.2020.101514
      [17]
      X. Feng, Q. Gao, and M.Y. Liu, Roller parameters prediction of steel tube based on principal component analysis and BP neural network, [in] 2018 Chinese Control and Decision Conference (CCDC), Shenyang, 2018, p. 4627.
      [18]
      A. Widener, Materials genome initiative, Chem. Eng. News Archive, 91(2013), No. 31, p. 25. doi: 10.1021/cen-09131-govpol1
      [19]
      J. Hachmann, R. Olivares-Amaya, S. Atahan-Evrenk, et al., The Harvard clean energy project: Large-scale computational screening and design of organic photovoltaics on the world community grid, J. Phys. Chem. Lett., 2(2011), No. 17, p. 2241. doi: 10.1021/jz200866s
      [20]
      J. Hachmann, R. Olivares-Amaya, A. Jinich, et al., Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry—The Harvard Clean Energy Project, Energy Environ. Sci., 7(2014), No. 2, p. 698. doi: 10.1039/C3EE42756K
      [21]
      A. Jain, S.P. Ong, G. Hautier, et al., Commentary: The Materials Project: A materials genome approach to accelerating materials innovation, APL Mater., 1(2013), No. 1, art. No. 011002. doi: 10.1063/1.4812323
      [22]
      J.E. Saal, S. Kirklin, M. Aykol, B. Meredig, and C. Wolverton, Materials design and discovery with high-throughput density functional theory: The open quantum materials database (OQMD), JOM, 65(2013), No. 11, p. 1501. doi: 10.1007/s11837-013-0755-4
      [23]
      S. Kirklin, J.E. Saal, B. Meredig, et al., The Open Quantum Materials Database (OQMD): Assessing the accuracy of DFT formation energies, Npj Comput. Mater., 1(2015), art. No. 15010. doi: 10.1038/npjcompumats.2015.10
      [24]
      S. Curtarolo, W. Setyawan, S.D. Wang, et al., AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations, Comput. Mater. Sci., 58(2012), p. 227. doi: 10.1016/j.commatsci.2012.02.002
      [25]
      F.H. Allen, The Cambridge Structural Database: A quarter of a million crystal structures and rising, Acta Crystallogr. B, 58(2002), No. 3, p. 380. doi: 10.1107/S0108768102003890
      [26]
      S.R. Kalidindi and M. De Graef, Materials data science: Current status and future outlook, Annu. Rev. Mater. Res., 45(2015), p. 171. doi: 10.1146/annurev-matsci-070214-020844
      [27]
      B. Sanchez-Lengeling and A. Aspuru-Guzik, Inverse molecular design using machine learning: Generative models for matter engineering, Science, 361(2018), No. 6400, p. 360. doi: 10.1126/science.aat2663
      [28]
      R.H. Taylor, F. Rose, C. Toher, et al., A RESTful API for exchanging materials data in the AFLOWLIB.org consortium, Comput. Mater. Sci., 93(2014), p. 178. doi: 10.1016/j.commatsci.2014.05.014
      [29]
      S.H. Lu, Q.H. Zhou, Y.X. Ouyang, Y.L. Guo, Q. Li, and J.L. Wang, Accelerated discovery of stable lead-free hybrid organic–inorganic perovskites via machine learning, Nat. Commun., 9(2018), No. 1, art. No. 3405. doi: 10.1038/s41467-018-05761-w
      [30]
      P. Raccuglia, K.C. Elbert, P.D.F. Adler, et al., Machine-learning-assisted materials discovery using failed experiments, Nature, 533(2016), No. 7601, p. 73. doi: 10.1038/nature17439
      [31]
      A.O. Oliynyk, E. Antono, T.D. Sparks, et al., High-throughput machine-learning-driven synthesis of full-Heusler compounds, Chem. Mater., 28(2016), No. 20, p. 7324. doi: 10.1021/acs.chemmater.6b02724
      [32]
      S.Y. Li, M. de Werk, L. St-Pierre, and M. Kumral, Dimensioning a stockpile operation using principal component analysis, Int. J. Miner. Metall. Mater., 26(2019), No. 12, p. 1485. doi: 10.1007/s12613-019-1849-y
      [33]
      A. Aspuru-Guzik, R. Lindh, and M. Reiher, The matter simulation (R)evolution, ACS Cent. Sci., 4(2018), No. 2, p. 144. doi: 10.1021/acscentsci.7b00550
      [34]
      P.B. Jørgensen, M.N. Schmidt, and O. Winther, Deep generative models for molecular science, Mol. Inform., 37(2018), No. 1-2, art. No. 1700133. doi: 10.1002/minf.201700133
      [35]
      M.I. Jordan and T.M. Mitchell, Machine learning: Trends, perspectives, and prospects, Science, 349(2015), No. 6245, p. 255. doi: 10.1126/science.aaa8415
      [36]
      N. Wagner and J.M. Rondinelli, Theory-guided machine learning in materials science, Front. Mater., 3(2016), art. No. 28.
      [37]
      H.D. Fu, H.T. Zhang, C.S. Wang, W. Yong, and J.X. Xie, Recent progress in the machine learning-assisted rational design of alloys, Int. J. Miner. Metall. Mater., 29(2022), No. 4, p. 635. doi: 10.1007/s12613-022-2458-8
      [38]
      C. Cortes and V. Vapnik, Support-vector networks, Mach. Learn., 20(1995), No. 3, p. 273.
      [39]
      S.J. Han, Q.B. Cao, and M. Han, Parameter selection in SVM with RBF kernel function, [in] World Automation Congress, Puerto Vallarta, 2012, p. 1.
      [40]
      N. Kireeva and V.S. Pervov, Materials space of solid-state electrolytes: Unraveling chemical composition-structure-ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches, Phys. Chem. Chem. Phys., 19(2017), No. 31, p. 20904. doi: 10.1039/C7CP00518K
      [41]
      D.E. Rumelhart, B. Widrow, and M.A. Lehr, The basic ideas in neural networks, Commun. ACM, 37(1994), No. 3, p. 87. doi: 10.1145/175247.175256
      [42]
      H.K.D.H. Bhadeshia, Neural networks in materials science, ISIJ Int., 39(1999), No. 10, p. 966. doi: 10.2355/isijinternational.39.966
      [43]
      A. Ghatak and P.S. Robi, Prediction of creep curve of HP40Nb steel using artificial neural network, Neural Comput. Appl., 30(2018), No. 9, p. 2953. doi: 10.1007/s00521-017-2851-9
      [44]
      S. Feng, H. Zhou, and H. 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
      [45]
      A.K. Jain, M.N. Murty, and P.J. Flynn, Data clustering: A review, ACM Comput. Surv., 31(1999), No. 3, p. 264. doi: 10.1145/331499.331504
      [46]
      G. Stein, B. Chen, A.S. Wu, and K.A. Hua, Decision tree classifier for network intrusion detection with GA-based feature selection, [in] Proceedings of the 43rd Annual Southeast Regional Conference, 2(2005), p. 136.
      [47]
      P. Zhang, Model selection via multifold cross validation, Ann. Statist., 21(1993), No. 1, p. 299.
      [48]
      R.J. Meijer and J.J. Goeman, Efficient approximate k-fold and leave-one-out cross-validation for ridge regression, Biom. J., 55(2013), No. 2, p. 141. doi: 10.1002/bimj.201200088
      [49]
      C.M. Bishop and N.M. Nasrabadi, Pattern Recognition and Machine Learning, Springer, New York, 2006.
      [50]
      F. Ajioka, Z.L. Wang, T. Ogawa, and Y. Adachi, Development of high accuracy segmentation model for microstructure of steel by deep learning, ISIJ Int., 60(2020), No. 5, p. 954. doi: 10.2355/isijinternational.ISIJINT-2019-568
      [51]
      C. Shen, C. Wang, M. Huang, N. Xu, S. van der Zwaag, and W. Xu, A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning, J. Mater. Sci. Technol., 93(2021), p. 191. doi: 10.1016/j.jmst.2021.04.009
      [52]
      B.L. DeCost, B. Lei, T. Francis, and E.A. Holm, High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel, Microsc. Microanal., 25(2019), No. 1, p. 21. doi: 10.1017/S1431927618015635
      [53]
      M.D. Hecht, B.A. Webler, and Y.N. Picard, Digital image analysis to quantify carbide networks in ultrahigh carbon steels, Mater. Charact., 117(2016), p. 134. doi: 10.1016/j.matchar.2016.04.012
      [54]
      N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern., 9(1979), No. 1, p. 62. doi: 10.1109/TSMC.1979.4310076
      [55]
      T. Serre, G. Kreiman, M. Kouh, C. Cadieu, U. Knoblich, and T. Poggio, A quantitative theory of immediate visual recognition, Prog. Brain Res., 165(2007), p. 33.
      [56]
      C. Kunselman, S. Sheikh, M. Mikkelsen, V. Attari, and R. Arróyave, Microstructure classification in the unsupervised context, Acta Mater., 223(2022), art. No. 117434. doi: 10.1016/j.actamat.2021.117434
      [57]
      S.W. Kim, S.H. Kang, S.J. Kim, and S. Lee, Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning, Sci. Rep., 11(2021), No. 1, art. No. 5902. doi: 10.1038/s41598-021-85407-y
      [58]
      X.Y. Huang, H. Wang, W.H. Xue, et al., A combined machine learning model for the prediction of time–temperature-transformation diagrams of high-alloy steels, J. Alloys Compd., 823(2020), art. No. 153694. doi: 10.1016/j.jallcom.2020.153694
      [59]
      X.X. Geng, Z. Cheng, S.Z. Wang, et al., A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design, J. Mater. Sci., 57(2022), No. 23, p. 10755. doi: 10.1007/s10853-022-07132-9
      [60]
      X. Jiang, B.R. Jia, G.F. Zhang, et al., A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data, Scr. Mater., 186(2020), p. 272. doi: 10.1016/j.scriptamat.2020.03.064
      [61]
      Q. Lu, S. Liu, W. Li, and X. Jin, Combination of thermodynamic knowledge and multilayer feedforward neural networks for accurate prediction of MS temperature in steels, Mater. Des., 192(2020), art. No. 108696. doi: 10.1016/j.matdes.2020.108696
      [62]
      C. Wang, K. Zhu, P. Hedström, Y. Li, and W. Xu, A generic and extensible model for the martensite start temperature incorporating thermodynamic data mining and deep learning framework, J. Mater. Sci. Technol., 128(2022), p. 31. doi: 10.1016/j.jmst.2022.04.014
      [63]
      K. Dehghani and A. Shafiei, Predicting the bake hardenability of steels using neural network modeling, Mater. Lett., 62(2008), No. 2, p. 173. doi: 10.1016/j.matlet.2007.04.114
      [64]
      X.Y. Huang, H. Wang, W.H. Xue, et al., Study on time-temperature-transformation diagrams of stainless steel using machine-learning approach, Comput. Mater. Sci., 171(2020), art. No. 109282. doi: 10.1016/j.commatsci.2019.109282
      [65]
      H.C. Kang, B.J. Park, J.H. Jang, K.S. Jang, and K.J. Lee, Determination of the continuous cooling transformation diagram of a high strength low alloyed steel, Met. Mater. Int., 22(2016), No. 6, p. 949. doi: 10.1007/s12540-016-6269-1
      [66]
      R. Chen, Z. Zheng, N. Li, J. Li, and F. Feng, In-situ investigation of phase transformation behaviors of 300M steel in continuous cooling process, Mater. Charact., 144(2018), p. 400. doi: 10.1016/j.matchar.2018.07.034
      [67]
      L. Qiao, J.C. Zhu, and Y. Wang, Machine learning-aided process design: Modeling and prediction of transformation temperature for pearlitic steel, Steel Res. Int., 93(2022), No. 1, art. No. 2100267. doi: 10.1002/srin.202100267
      [68]
      Z. Jančíková, V. Roubíček, and D. Juchelková, Application of artificial intelligence methods for prediction of steel mechanical properties, Metalurgija, 47(2008), No. 4, art. No. 339.
      [69]
      X. Wei, S. van der Zwaag, Z. Jia, C. Wang, and W. Xu, On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets, Acta Mater., 235(2022), art. No. 118103. doi: 10.1016/j.actamat.2022.118103
      [70]
      J.L. Du, Y.L. Feng, and M. Zhang, Construction of a machine-learning-based prediction model for mechanical properties of ultra-fine-grained Fe–C alloy, J. Mater. Res. Technol., 15(2021), p. 4914. doi: 10.1016/j.jmrt.2021.10.111
      [71]
      J. Li, J.H. Cheng, J.Y. Shi, and F. Huang, Brief introduction of back propagation (BP) neural network algorithm and its improvement, [in] Advances in Computer Science and Information Engineering, Springer, Berlin, Heidelberg, 2012, p. 553.
      [72]
      Y. Diao, L. Yan, and K. Gao, A strategy assisted machine learning to process multi-objective optimization for improving mechanical properties of carbon steels, J. Mater. Sci. Technol., 109(2022), p. 86. doi: 10.1016/j.jmst.2021.09.004
      [73]
      J. Lee Rodgers and W.A. Nicewander, Thirteen ways to look at the correlation coefficient, Am. Stat., 42(1988), No. 1, p. 59.
      [74]
      T.Y. Chen, L.F. He, M.H. Cullison, et al., The correlation between microstructure and nanoindentation property of neutron-irradiated austenitic alloy D9, Acta Mater., 195(2020), p. 433. doi: 10.1016/j.actamat.2020.05.020
      [75]
      Z. Guo, W. Sha, and D. Vaumousse, Microstructural evolution in a PH13-8 stainless steel after ageing, Acta Mater., 51(2003), No. 1, p. 101. doi: 10.1016/S1359-6454(02)00353-1
      [76]
      I.D. Jung, D.S. Shin, D. Kim, et al., Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels, Materialia, 11(2020), art. No. 100699. doi: 10.1016/j.mtla.2020.100699
      [77]
      Z.L. Wang and Y. Adachi, Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach, Mater. Sci. Eng. A, 744(2019), p. 661. doi: 10.1016/j.msea.2018.12.049
      [78]
      Y. Adachi, N. Sato, M. Ojima, M. Nakayama, and Y.T. Wang, Development of fully automated serial-sectioning 3D microscope and topological approach to pearlite and dual-phase microstructure in steels, [in] Proceedings of the 1st International Conference on 3D Materials Science, Pennsylvania, 2012, p. 37.
      [79]
      T. Dietterich, Overfitting and undercomputing in machine learning, ACM Comput. Surv., 27(1995), No. 3, p. 326. doi: 10.1145/212094.212114
      [80]
      H. Golnabi and A. Asadpour, Design and application of industrial machine vision systems, Robotics Comput. Integr. Manuf., 23(2007), No. 6, p. 630. doi: 10.1016/j.rcim.2007.02.005
      [81]
      F.A. Saiz, I. Serrano, I. Barandiarán, and J.R. Sánchez, A robust and fast deep learning-based method for defect classification in steel surfaces, [in] 2018 International Conference on Intelligent Systems (IS), Funchal, 2018, p. 455.
      [82]
      K. Song and Y. Yan, A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects, Appl. Surf. Sci., 285(2013), p. 858. doi: 10.1016/j.apsusc.2013.09.002
      [83]
      A. Krizhevsky, I. Sutskever, and G.E. Hinton, ImageNet classification with deep convolutional neural networks, Commun. ACM, 60(2017), No. 6, p. 84. doi: 10.1145/3065386
      [84]
      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
      [85]
      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
      [86]
      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. Sin., 57(2021), No. 11, p. 1343.
      [87]
      N. Baluc, D.S. Gelles, S. Jitsukawa, et al., Status of reduced activation ferritic/martensitic steel development, J. Nucl. Mater., 367-370(2007), p. 33. doi: 10.1016/j.jnucmat.2007.03.036
      [88]
      X. Li, M. Zheng, X. Yang, P. Chen, and W. Ding, A property-oriented design strategy of high-strength ductile RAFM steels based on machine learning, Mater. Sci. Eng. A, 840(2022), art. No. 142891. doi: 10.1016/j.msea.2022.142891
      [89]
      N. Chaudhary, A. Abu-Odeh, I. Karaman, and R. Arróyave, A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels, J. Mater. Sci., 52(2017), No. 18, p. 11048. doi: 10.1007/s10853-017-1252-x
      [90]
      B. Nenchev, Q. Tao, Z.H. Dong, et al., Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability, Int. J. Miner. Metall. Mater., 29(2022), No. 4, p. 836. doi: 10.1007/s12613-022-2437-0
      [91]
      S.L. Liu, Y.J. Su, H.Q. Yin, et al., An infrastructure with user-centered presentation data model for integrated management of materials data and services, Npj Comput. Mater., 7(2021), art. No. 88. doi: 10.1038/s41524-021-00557-x
      [92]
      R. Agarwal and V. Dhar, Big data, data science, and analytics: The opportunity and challenge for IS research, Inf. Syst. Res., 25(2014), No. 3, p. 443. doi: 10.1287/isre.2014.0546
      [93]
      B.Y. Ma, X.Y. Wei, C.N. Liu, et al., Data augmentation in microscopic images for material data mining, Npj Comput. Mater., 6(2020), art. No. 125. doi: 10.1038/s41524-020-00392-6
      • Relative Articles
      Cited By

    Catalog


    • /

      返回文章
      返回

      版权所有 ©《矿物冶金与材料学报（英文）》编辑部

      地址：北京市海淀区学院路30号邮政编码：100083

      电子邮箱：journal@ustb.edu.cn电话：+86-10-62332875

      本系统由 北京仁和汇智信息技术有限公司 开发    百度统计