综述：合金设计中物理模型与人工智能的集成与发展

doi:10.11900/0412.1961.2024.00355

金属学报

2025, Vol. 61

Issue (4): 541-560 DOI: 10.11900/0412.1961.2024.00355

综述

本期目录 | 过刊浏览

综述：合金设计中物理模型与人工智能的集成与发展

王晨充, 徐伟(

)

东北大学数字钢铁全国重点实验室沈阳 110819

Overview: Integration and Development of Physical Models and Artificial Intelligence in Alloy Design

WANG Chenchong, XU Wei(

)

State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China

引用本文:

王晨充, 徐伟. 综述：合金设计中物理模型与人工智能的集成与发展[J]. 金属学报, 2025, 61(4): 541-560.
Chenchong WANG, Wei XU. Overview: Integration and Development of Physical Models and Artificial Intelligence in Alloy Design[J]. Acta Metall Sin, 2025, 61(4): 541-560.

全文: PDF(2632 KB) HTML

摘要:

随着第四科学范式数据驱动方法的兴起，其在合金设计领域中对物理模型驱动的第三科学范式构成了显著的冲击。但2种范式均无法在合金设计，尤其是宏观力学性能设计层面打破模型准确性与可解释性间的互斥关系，因而无法满足材料基因工程领域，尤其是针对块体金属结构材料设计的高效率、高合理性要求，这一困境也催生了第五范式AI4Sci在该领域的兴起。本文综述了物理冶金原理指导人工智能方法体系下的诸多案例，从数值数据指导、图像数据指导和机制指导3个层次系统阐明如何进行物理模型/机制与人工智能的深度结合，以打破合金设计过程中模型准确性与可解释性间的互斥关系，从而深刻揭示合金设计过程中多尺度物理模型、人工智能及AI4Sci三大范式的理论本质与优劣势，并在突破跨尺度建模困境、发展材料学大模型技术等方向上，给予各科学范式在合金设计中未来发展的研究思路与技术方法指导。

关键词 ：合金设计, 材料基因工程, 人工智能, 跨尺度建模, AI4Sci

Abstract：

With the rise of data-driven methods as the fourth scientific paradigm, their impact on the third paradigm—physical model-driven approaches—has been significant in the field of alloy design. However, neither paradigm can overcome the trade-off between model accuracy and interpretability, particularly in mechanical performance design. As a consequence, they fail to meet the efficiency and rationality requirements necessary for alloy development within Material Genome Engineering, especially for metal structural materials. This challenge has led to the emergence of the fifth paradigm, AI4Sci, in alloy design. This article provides an overview of various cases employing the physical metallurgy-guided artificial intelligence method system. It systematically explains how to integrate physical models and mechanisms with artificial intelligence at three levels: numerical data guidance, image data guidance, and mechanism guidance. This approach aims to resolve the inherent trade-off between accuracy and interpretability in alloy design. In addition, it explores the theoretical foundations, advantages, and limitations of three paradigms—multi-scale physical models, artificial intelligence, and AI4Sci—within the field. For cross-scale modeling and materials science large models, this article offers insights into conceptual frameworks and technical methodologies for the future development of each scientific paradigm in alloy design.

Key words： alloy design material genome engineering artificial intelligence cross scale modeling AI4Sci

收稿日期: 2024-10-22

ZTFLH:

TG111.8

基金资助:国家重点研发计划项目(2023YFB3712403);国家自然科学基金项目(U22A20106, 52311530082)

通讯作者: 徐伟，xuwei@ral.neu.edu.cn，主要从事金属材料基因工程研究

Corresponding author: XU Wei, professor, Tel: (024)83680246, E-mail: xuwei@ral.neu.edu.cn

作者简介: 王晨充，男，1988年生，副教授，博士

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https://www.ams.org.cn/CN/10.11900/0412.1961.2024.00355 或 https://www.ams.org.cn/CN/Y2025/V61/I4/541

图1 模型可解释性与准确性的互斥关系本质

图2 降维处理方法对模型准确性的量化影响[29~32]

图3 物理冶金原理指导人工智能的跨尺度体系

图4 马氏体相变开始温度(Ms)计算模型准确性与域外扩展能力对比[43]

图5 热力学机制信息指导人工智能的合金设计范式[10,46~54]

图6 多尺度机制信息指导人工智能的设计范式[60~67]

图7 图像核心信息指导人工智能的算法对比[73~75]

图8 各多模态算法框架及其优缺点

图9 各图像算法的机制关联与数据量需求[73,76,87,90]

图10 机制指导人工智能的方法与应用

图11 五个科学范式在合金设计领域的未来发展方向

1	Liao M Q, Wang Y, Wang Y, et al. Zentropy theory: Bridging materials gene to materials properties [J]. Acta Metall. Sin., 2024, 60: 1379 doi: 10.11900/0412.1961.2024.00147
1	廖名情, 王毅, 王义等. 叠熵理论: 从材料基因到材料性能 [J]. 金属学报, 2024, 60: 1379 doi: 10.11900/0412.1961.2024.00147
2	Xie J X, Su Y J, Xue D Z, et al. Machine learning for materials research and development [J]. Acta Metall. Sin., 2021, 57: 1343 doi: 10.11900/0412.1961.2021.00357
2	谢建新, 宿彦京, 薛德祯等. 机器学习在材料研发中的应用 [J]. 金属学报, 2021, 57: 1343 doi: 10.11900/0412.1961.2021.00357
3	Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science [J]. APL Mater., 2016, 4: 053208
4	Peng G C Y, Alber M, Tepole A B, et al. Multiscale modeling meets machine learning: What can we learn? [J]. Arch. Comput. Methods Eng., 2021, 28: 1017
5	Su Y J, Fu H D, Bai Y, et al. Progress in materials genome engineering in China [J]. Acta Metall. Sin., 2020, 56: 1313 doi: 10.11900/0412.1961.2020.00199
5	宿彦京, 付华栋, 白洋等. 中国材料基因工程研究进展 [J]. 金属学报, 2020, 56: 1313 doi: 10.11900/0412.1961.2020.00199
6	Zhang X, Wang L M, Helwig J, et al. Artificial intelligence for science in quantum, atomistic, and continuum systems [DB/OL]. arXiv: 2307. 08423, 2023
7	Cui P, Athey S. Stable learning establishes some common ground between causal inference and machine learning [J]. Nat. Mach. Intell., 2022, 4: 110
8	Zhong X T, Gallagher B, Liu S S, et al. Explainable machine learning in materials science [J]. npj Comput. Mater., 2022, 8: 204
9	Shen C G, Wang C C, Rivera-Díaz-del-Castillo P E J, et al. Discovery of marageing steels: Machine learning vs. physical metallurgical modelling [J]. J. Mater. Sci. Technol., 2021, 87: 258
10	Shen C G, Wang C C, Wei X L, et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel [J]. Acta Mater., 2019, 179: 201
11	Guo A X Y, Cheng L J, Zhan S, et al. Biomedical applications of the powder‐based 3D printed titanium alloys: A review [J]. J. Mater. Sci. Technol., 2022, 125: 252 doi: 10.1016/j.jmst.2021.11.084
12	Cisternas L A, Lucay F A, Botero Y L. Trends in modeling, design, and optimization of multiphase systems in minerals processing [J]. Minerals, 2020, 10: 22
13	Huang L, Ruan S, Xing Y C, et al. A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods [J]. Med. Image Anal., 2024, 97: 103223
14	Szymanski N J, Rendy B, Fei Y X, et al. An autonomous laboratory for the accelerated synthesis of novel materials [J]. Nature, 2023, 624: 86
15	Xue D Z, Balachandran P V, Hogden J, et al. Accelerated search for materials with targeted properties by adaptive design [J]. Nat. Commun., 2016, 7: 11241 doi: 10.1038/ncomms11241 pmid: 27079901
16	Zhao W C, Zheng C, Xiao B, et al. Composition refinement of 6061 aluminum alloy using active machine learning model based on Bayesian optimization sampling [J]. Acta Metall. Sin., 2021, 57: 797 doi: 10.11900/0412.1961.2020.00298
16	赵婉辰, 郑晨, 肖斌等. 基于Bayesian采样主动机器学习模型的6061铝合金成分精细优化 [J]. 金属学报, 2021, 57: 797 doi: 10.11900/0412.1961.2020.00298
17	Gawlikowski J, Tassi C R N, Ali M, et al. A survey of uncertainty in deep neural networks [J]. Artif. Intell. Rev., 2023, 56(suppl.1) : 1513
18	Kurz A, Hauser K, Mehrtens H A, et al. Uncertainty estimation in medical image classification: Systematic review [J]. JMIR Med. Inf., 2022, 10: e36427
19	Jospin L V, Laga H, Boussaid F, et al. Hands-on Bayesian neural networks—A tutorial for deep learning users [J]. IEEE Comput. Intell. Mag., 2022, 17: 29
20	Venkatraman A, de Oca Zapiain D M, Kalidindi S R. Reduced-order models for ranking damage initiation in dual-phase composites using Bayesian neural networks [J]. JOM, 2020, 72: 4359 doi: 10.1007/s11837-020-04387-y
21	Wu S W, Zhou X G, Chen Q Y, et al. Development of constitutive models for extrapolative prediction of Nb-Ti micro alloyed steel [J]. Steel Res. Int., 2017, 88: 1700082
22	Muth A, Venkatraman A, John R, et al. Neighborhood spatial correlations and machine learning classification of fatigue hot-spots in Ti-6Al-4V [J]. Mech. Mater., 2023, 182: 104679
23	He Z C, Huo S L, Li E, et al. Data-driven approach to characterize and optimize properties of carbon fiber non-woven composite materials [J]. Compos. Struct., 2022, 297: 115961
24	Norris C, Ayyaswamy A, Vishnugopi B S, et al. Uncertainty quantification and propagation in lithium-ion battery electrodes using Bayesian convolutional neural networks [J]. Energy Storage Mater., 2024, 67: 103251
25	Ali S, Abuhmed T, El-Sappagh S, et al. Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence [J]. Inf. Fusion, 2023, 99: 101805
26	Hassija V, Chamola V, Mahapatra A, et al. Interpreting black-box models: A review on explainable artificial intelligence [J]. Cogn. Comput., 2024, 16: 45
27	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [A]. 31st International Conference on Neural Information Processing Systems [C]. Long Beach: Curran Associates Inc., 2017
28	Xu P C, Ji X B, Li M J, et al. Small data machine learning in materials science [J]. npj Comput. Mater., 2023, 9: 42
29	Ikram S T, Cherukuri A K. Improving accuracy of intrusion detection model using PCA and optimized SVM [J]. J. Comput. Inf. Technol., 2016, 24: 133
30	He F, Zhang L Y. Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network [J]. J. Process Control, 2018, 66: 51
31	Xue J L, Chen Y C, Li O, et al. Classification and identification of unknown network protocols based on CNN and T-SNE [J]. J. Phys.: Conf. Ser., 2020, 1617: 012071
32	Gallos I K, Gkiatis K, Matsopoulos G K, et al. ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia [J]. AIMS Neurosci., 2021, 8: 295 doi: 10.3934/Neuroscience.2021016 pmid: 33709030
33	Jiang L, Fu H D, Zhang H T, et al. Physical mechanism interpretation of polycrystalline metals' yield strength via a data-driven method: A novel Hall-Petch relationship [J]. Acta Mater., 2022, 231: 117868
34	Baskes M I. The status role of modeling and simulation in materials science and engineering [J]. Curr. Opin. Solid State Mater. Sci., 1999, 4: 273
35	Elliott J A. Novel approaches to multiscale modelling in materials science [J]. Int. Mater. Rev., 2011, 56: 207
36	Chen Z, Haykin S. On different facets of regularization theory [J]. Neural Comput., 2002, 14: 2791 pmid: 12487794
37	Lu Q, Xu W, van der Zwaag S. The design of a compositionally robust martensitic creep-resistant steel with an optimized combination of precipitation hardening and solid-solution strengthening for high-temperature use [J]. Acta Mater., 2014, 77: 310
38	Wang C C, Wei X L, Ren D, et al. High-throughput map design of creep life in low-alloy steels by integrating machine learning with a genetic algorithm [J]. Mater. Des., 2022, 213: 110326
39	Li Y, Martín D S, Wang J L, et al. A review of the thermal stability of metastable austenite in steels: Martensite formation [J]. J. Mater. Sci. Technol., 2021, 91: 200 doi: 10.1016/j.jmst.2021.03.020
40	Stormvinter A, Borgenstam A, Ågren J. Thermodynamically based prediction of the martensite start temperature for commercial steels [J]. Metall. Mater. Trans., 2012, 43A: 3870
41	Lee S J, Park K S. Prediction of martensite start temperature in alloy steels with different grain sizes [J]. Metall. Mater. Trans., 2013, 44A: 3423
42	Rahaman M, Mu W Z, Odqvist J, et al. Machine learning to predict the martensite start temperature in steels [J]. Metall. Mater. Trans., 2019, 50A: 2081
43	Wang C C, Zhu K Y, Hedström P, et al. A generic and extensible model for the martensite start temperature incorporating thermodynamic data mining and deep learning framework [J]. J. Mater. Sci. Technol., 2022, 128: 31 doi: 10.1016/j.jmst.2022.04.014
44	Ghosh G, Olson G B. Kinetics of F.C.C. → B.C.C. heterogeneous martensitic nucleation—I. The critical driving force for athermal nucleation [J]. Acta Metall. Mater., 1994, 42: 3361
45	Lu Q, Liu S L, Li W, et al. Combination of thermodynamic knowledge and multilayer feedforward neural networks for accurate prediction of M_S temperature in steels [J]. Mater. Des., 2020, 192: 108696
46	Kannan R, Nandwana P. Accelerated alloy discovery using synthetic data generation and data mining [J]. Scr. Mater., 2023, 228: 115335
47	Zhang S J, Yi W, Zhong J, et al. Computer alloy design of Ti modified Al-Si-Mg-Sr casting alloys for achieving simultaneous enhancement in strength and ductility [J]. Materials, 2023, 16: 306
48	Zou H, Tian Y Y, Zhang L G, et al. Integrating machine learning and CALPHAD method for exploring low-modulus near-β-Ti alloys [J]. Rare Met., 2024, 43: 309
49	Fu H, Gao T C, Gao J B, et al. Breaking hardness and electrical conductivity trade-off in Cu-Ti alloys through machine learning and Pareto front [J]. Mater. Res. Lett., 2024, 12: 580
50	Zhang W L, Tang Y, Gao J H, et al. Determination of hardness and Young's modulus in fcc Cu-Ni-Sn-Al alloys via high-throughput experiments, CALPHAD approach and machine learning [J]. J. Mater. Res. Technol., 2024, 30: 5381
51	Liu X L, Zhang J X, Pei Z R. Machine learning for high-entropy alloys: Progress, challenges and opportunities [J]. Prog. Mater. Sci., 2023, 131: 101018
52	Xu B, Yin H Q, Jiang X, et al. Data-driven design of Ni-based turbine disc superalloys to improve yield strength [J]. J. Mater. Sci. Technol., 2023, 155: 175 doi: 10.1016/j.jmst.2023.01.032
53	Lu S, Zou M, Zhang X R, et al. Data-driven “cross-component” design and optimization of γ′-strengthened Co-based superalloys [J]. Adv. Eng. Mater., 2023, 25: 2201257
54	Trehern W, Ortiz-Ayala R, Atli K C, et al. Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework [J]. Acta Mater., 2022, 228: 117751
55	Zeng Y Z, Man M R, Bai K W, et al. Explore the full temperature-composition space of 20 quinary CCAs for FCC and BCC single-phases by an iterative machine learning + CALPHAD method [J]. Acta Mater., 2022, 231: 117865
56	Jin X Z, Luo H, Wang X F, et al. Data mining accelerated the design strategy of high-entropy alloys with the largest hardness based on genetic algorithm optimization [J]. MGE Adv., 2024, 2: e49
57	Vazquez G, Singh P, Sauceda D, et al. Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys [J]. Acta Mater., 2022, 232: 117924
58	Wen C, Wang C X, Zhang Y, et al. Modeling solid solution strengthening in high entropy alloys using machine learning [J]. Acta Mater., 2021, 212: 116917
59	Li Z, Nash W T, O'Brien S P, et al. cardiGAN: A generative adversarial network model for design and discovery of multi principal element alloys [J]. J. Mater. Sci. Technol., 2022, 125: 81 doi: 10.1016/j.jmst.2022.03.008
60	Zhang H T, Fu H D, Zhu S C, et al. Machine learning assisted composition effective design for precipitation strengthened copper alloys [J]. Acta Mater., 2021, 215: 117118
61	He J J, Li J J, Liu C B, et al. Machine learning identified materials descriptors for ferroelectricity [J]. Acta Mater., 2021, 209: 116815
62	Hartnett T Q, Sharma V, Garg S, et al. Accelerated design of MTX alloys with targeted magnetostructural properties through interpretable machine learning [J]. Acta Mater., 2022, 231: 117891
63	Wang C C, Zhang Z, Jing X Y, et al. Optimization of multistage femtosecond laser drilling process using machine learning coupled with molecular dynamics [J]. Opt. Laser Technol., 2022, 156: 108442
64	Zhang Z, Yang Z N, Wang C C, et al. Accelerating ultrashort pulse laser micromachining process comprehensive optimization using a machine learning cycle design strategy integrated with a physical model [J]. J. Intell. Manuf., 2024, 35: 449
65	Mondal B, Mukherjee T, DebRoy T. Crack free metal printing using physics informed machine learning [J]. Acta Mater., 2022, 226: 117612
66	Rao Z Y, Tung P Y, Xie R W, et al. Machine learning-enabled high-entropy alloy discovery [J]. Science, 2022, 378: 78 doi: 10.1126/science.abo4940 pmid: 36201584
67	Zou C X, Li J S, Wang W Y, et al. Integrating data mining and machine learning to discover high-strength ductile titanium alloys [J]. Acta Mater., 2021, 202: 211
68	Holm E A, Cohn R, Gao N, et al. Overview: Computer vision and machine learning for microstructural characterization and analysis [J]. Metall. Mater. Trans., 2020, 51A: 5985
69	Müller M, Stiefel M, Bachmann B I, et al. Overview: Machine learning for segmentation and classification of complex steel microstructures [J]. Metals, 2024, 14: 553
70	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [A]. 3rd International Conference on Learning Representations [C]. San Diego, May 7-9, 2015
71	Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation [A]. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention [C]. Munich: Springer, 2015
72	He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition [A]. 2016 IEEE Conference on Computer Vision and Pattern Recognition [C]. Las Vegas: IEEE, 2016: 770
73	Shen C G, Wang C C, Huang M H, et al. A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning [J]. J. Mater. Sci. Technol., 2021, 93: 191 doi: 10.1016/j.jmst.2021.04.009
74	Kunselman C, Sheikh S, Mikkelsen M, et al. Microstructure classification in the unsupervised context [J]. Acta Mater., 2022, 223: 117434
75	Ma X D, Zhang Y Q, Wang C C, et al. Alloy microstructure segmentation through SAM and domain knowledge without extra training [J]. Scr. Mater., 2025, 260: 116581
76	Ren D, Wang C C, Wei X L, et al. Building a quantitative composition-microstructure-property relationship of dual-phase steels via multimodal data mining [J]. Acta Mater., 2023, 252: 118954
77	Zhao P L, Wang Y W, Jiang B Y, et al. Neural network modeling of titanium alloy composition-microstructure-property relationships based on multimodal data [J]. Mater. Sci. Eng., 2023, A879: 145202
78	Wang C C, Ren D, Li Y, et al. Prediction of deformation-induced martensite start temperature by convolutional neural network with dual mode features [J]. Materials, 2022, 15: 3495
79	Han S Y, Wang C C, Lai Q Q, et al. Fitting-free mechanical response prediction in dual-phase steels by crystal plasticity theory guided deep learning [J]. Acta Mater., 2025, Available online, 120936
80	Jin L C, Tan F X, Jiang S M. Generative adversarial network technologies and applications in computer vision [J]. Comput. Intel. Neurosci., 2020, 2020: 1459107
81	Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets [A]. Proceedings of the 28th International Conference on Neural Information Processing Systems [C]. Montreal: MIT Press, 2014
82	Qian C, Tan R K, Ye W J. Design of architectured composite materials with an efficient, adaptive artificial neural network-based generative design method [J]. Acta Mater., 2022, 225: 117548
83	Narikawa R, Fukatsu Y, Wang Z L, et al. Generative adversarial networks-based synthetic microstructures for data-driven materials design [J]. Adv. Theory Simul., 2022, 5: 2100470
84	Long T, Zhang Y X, Fortunato N M, et al. Inverse design of crystal structures for multicomponent systems [J]. Acta Mater., 2022, 231: 117898
85	Yang X. A machine learning-based approach for materials microstructure analysis and prediction [D]. Houston City: Rice University, 2020
86	Cao Z H, Liu Q, Liu Q C, et al. A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V [J]. npj Comput. Mater., 2023, 9: 195
87	Yang Z J, Li X L, Brinson L C, et al. Microstructural materials design via deep adversarial learning methodology [J]. J. Mech. Des., 2018, 140: 111416
88	Kingma D P, Welling M. Auto-encoding variational Bayes [A]. 2nd International Conference on Learning Representations [C]. Banff, April 14-16, 2014
89	Pei Z R, Rozman K A, Doğan Ö N, et al. Machine-learning microstructure for inverse material design [J]. Adv. Sci., 2021, 8: 2101207
90	Ma X D, Zhang Y Q, Wang C C, et al. Creating a microstructure latent space with rich material information for multiphase alloy design [DB/OL]. arXiv: 2409. 02648, 2024
91	Jha D, Choudhary K, Tavazza F, et al. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning [J]. Nat. Commun., 2019, 10: 5316 doi: 10.1038/s41467-019-13297-w pmid: 31757948
92	Yamada H, Liu C, Wu S, et al. Predicting materials properties with little data using shotgun transfer learning [J]. ACS Cent. Sci., 2019, 5: 1717
93	Wei X L, van der Zwaag S, Jia Z X, et al. 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 [J]. Acta Mater., 2022, 235: 118103
94	Jiang L, Zhang Z H, Hu H, et al. A rapid and effective method for alloy materials design via sample data transfer machine learning [J]. npj Comput. Mater., 2023, 9: 26
95	Wei X L, Zhang C, Han S Y, et al. High cycle fatigue S-N curve prediction of steels based on transfer learning guided long short term memory network [J]. Int. J. Fatigue, 2022, 163: 107050
96	Cuomo S, Di Cola V S, Giampaolo F, et al. Scientific machine learning through physics-informed neural networks: Where we are and what's next [J]. J. Sci. Comput., 2022, 92: 88
97	Liu C, Wu H A. A variational formulation of physics-informed neural network for the applications of homogeneous and heterogeneous material properties identification [J]. Int. J. Appl. Mech., 2023, 15: 23500655
98	Jin H X, Zhang E R, Espinosa H D. Recent advances and applications of machine learning in experimental solid mechanics: A review [J]. Appl. Mech. Rev., 2023, 75: 061001
99	Zhang L J, Li K W, Wang H, et al. MFC-PINN: A method to improve the accuracy and robustness of acoustic emission source planar localization [J]. Measurement, 2024, 235: 114995
100	Liu T Y. AI for Science: Pursuing the brightest side of human intelligence [EB/OL]. (2023-01-05).
100	刘铁岩. AI for Science: 追求人类智能最光辉的一面[EB/OL]. (2023-01-05).

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[13]	单智伟, 刘博宇. Mg的{101̅2}形变孪晶机制^*[J]. 金属学报, 2016, 52(10): 1267-1278.
[14]	张新明, 邓运来, 张勇. 高强铝合金的发展及其材料的制备加工技术[J]. 金属学报, 2015, 51(3): 257-271.
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