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

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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.

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References

  1. G.B. Olson, Genomic materials design: The ferrous frontier, Acta Mater., 61(2013), No. 3, p. 771.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

  11. N. Nosengo and G. Ceder, Can artificial intelligence create the next wonder material, Nature, 533(2016), No. 7601, p. 22.

    Article  CAS  Google Scholar 

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

  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.

    Article  CAS  Google Scholar 

  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.

  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.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  26. S.R. Kalidindi and M. De Graef, Materials data science: Current status and future outlook, Annu. Rev. Mater. Res., 45(2015), p. 171.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  33. A. Aspuru-Guzik, R. Lindh, and M. Reiher, The matter simulation (R)evolution, ACS Cent. Sci., 4(2018), No. 2, p. 144.

    Article  CAS  Google Scholar 

  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.

  35. M.I. Jordan and T.M. Mitchell, Machine learning: Trends, perspectives, and prospects, Science, 349(2015), No. 6245, p. 255.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  38. C. Cortes and V. Vapnik, Support-vector networks, Mach. Learn., 20(1995), No. 3, p. 273.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  41. D.E. Rumelhart, B. Widrow, and M.A. Lehr, The basic ideas in neural networks, Commun. ACM, 37(1994), No. 3, p. 87.

    Article  Google Scholar 

  42. H.K.D.H. Bhadeshia, Neural networks in materials science, ISIJ Int., 39(1999), No. 10, p. 966.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  45. A.K. Jain, M.N. Murty, and P.J. Flynn, Data clustering: A review, ACM Comput. Surv., 31(1999), No. 3, p. 264.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  49. C.M. Bishop and N.M. Nasrabadi, Pattern Recognition and Machine Learning, Springer, New York, 2006.

    Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  54. N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern., 9(1979), No. 1, p. 62.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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.

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

  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.

    Article  CAS  Google Scholar 

  63. K. Dehghani and A. Shafiei, Predicting the bake hardenability of steels using neural network modeling, Mater. Lett., 62(2008), No. 2, p. 173.

    Article  CAS  Google Scholar 

  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.

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

  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.

  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.

    Article  CAS  Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  73. J. Lee Rodgers and W.A. Nicewander, Thirteen ways to look at the correlation coefficient, Am. Stat., 42(1988), No. 1, p. 59.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

  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.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  80. H. Golnabi and A. Asadpour, Design and application of industrial machine vision systems, Robotics Comput. Integr. Manuf., 23(2007), No. 6, p. 630.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  83. A. Krizhevsky, I. Sutskever, and G.E. Hinton, ImageNet classification with deep convolutional neural networks, Commun. ACM, 60(2017), No. 6, p. 84.

    Article  Google Scholar 

  84. A. Zunger, Inverse design in search of materials with target functionalities, Nat. Rev. Chem., 2(2018), art. No. 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.

    Article  CAS  Google Scholar 

  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.

    CAS  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Nos. 52122408, 52071023, 51901013, and 52101019) and the Fundamental Research Funds for the Central Universities (University of Science and Technology Beijing, Nos. FRF-TP-2021-04C1 and 06500135). The computing work is supported by USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering.

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  1. Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Guangfei Pan, Feiyang Wang, Chunlei Shang, Honghui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao & Xinping Mao

  2. Yangjiang Branch, Guangdong Laboratory for Materials Science and Technology (Yangjiang Advanced Alloys Laboratory), Yangjiang, 529500, China

    Honghui Wu, Guilin Wu, Junheng Gao, Shuize Wang & Xinping Mao

  3. Guangdong Province Key Laboratory of Durability for Marine Civil Engineering, School of Civil Engineering, Shenzhen University, Shenzhen, 518060, China

    Xiaoye Zhou

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Pan, G., Wang, F., Shang, C. et al. Advances in machine learning- and artificial intelligence-assisted material design of steels. Int J Miner Metall Mater 30, 1003–1024 (2023). https://doi.org/10.1007/s12613-022-2595-0

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