机器学习在材料研发中的应用

doi:10.11900/0412.1961.2021.00357

金属学报

2021, Vol. 57

Issue (11): 1343-1361 DOI: 10.11900/0412.1961.2021.00357

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机器学习在材料研发中的应用

谢建新¹(

), 宿彦京¹(

), 薛德祯², 姜雪¹, 付华栋¹, 黄海友¹

^1.北京科技大学新材料技术研究院北京材料基因工程高精尖创新中心北京 100083
^2.西安交通大学金属材料强度国家重点实验室西安 710049

Machine Learning for Materials Research and Development

XIE Jianxin¹(

), SU Yanjing¹(

), XUE Dezhen², JIANG Xue¹, FU Huadong¹, HUANG Haiyou¹

^1.Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
^2.State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China

引用本文:

谢建新, 宿彦京, 薛德祯, 姜雪, 付华栋, 黄海友. 机器学习在材料研发中的应用[J]. 金属学报, 2021, 57(11): 1343-1361.
Jianxin XIE, Yanjing SU, Dezhen XUE, Xue JIANG, Huadong FU, Haiyou HUANG. Machine Learning for Materials Research and Development[J]. Acta Metall Sin, 2021, 57(11): 1343-1361.

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摘要:

大数据和人工智能技术的快速发展推动数据驱动的材料研发快速发展成为变革传统试错法的新模式，即所谓的材料研发第四范式。新模式将大幅度提升材料研发效率和工程化应用水平，推动新材料快速发展。本文聚焦机器学习辅助材料研发这一新兴领域，以材料预测和优化设计为主线，在简述材料特征构建与筛选的基础上，综述了机器学习在材料相结构、显微组织、成分-工艺-性能、服役行为预测等方面的研究进展；针对材料数据样本量少、噪音高、质量差，以及新材料探索空间巨大的特点，综述了机器学习模型与优化算法和策略融合，在新材料优化设计中的研究进展和典型应用。最后，讨论了机器学习在材料领域的发展机遇和挑战，展望了发展前景。

关键词 ：材料数据, 数据挖掘, 机器学习, 材料设计, 材料基因工程

Abstract：

The rapid advancement of big data and artificial intelligence has resulted in new data-driven materials research and development (R&D), which has achieved substantial progress. This fourth paradigm is believed to improve materials design efficiency and industrialized application and stimulate the discovery of new materials. The focus of this work is on the emerging field of machine learning-assisted material R&D, with an emphasis on machine learning predictions and optimization design. Following a brief description of feature construction and selection, recent developments in material predictions on phases/structures, processing-structure-property relationships, microstructure, and material performance are reviewed. This paper also summarizes the research progress on optimization algorithms with machine learning models, which is expected to overcome the bottlenecks such as the small size and high noise level of material data samples and huge space for exploration. The challenges and future opportunities for machine learning applications in materials R&D are discussed and prospected.

Key words： materials data data mining machine learning material design material genome engineering

收稿日期: 2021-08-25

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图1 材料高通量实验结合机器学习预测镍基高温合金的有害相[19]

图2 融合显微组织深度学习的钢铁材料组织和性能设计流程[45]

图3 相变诱导塑性(TRIP)钛合金力学性能的机器学习流程[60]

图4 从可解释机器学习中提取到决策树和回归模型[78]

图5 高熵合金固溶强化模型精度(MRE)的对比[80](a) the new model (b) S-model (c) T-model (d) V-model(e) comparison of predicted and experimental solid solution strength (ΔσSSS) (f) the revised model

图6 机器学习模型与遗传算法结合的新钢种设计策略[116]

图7 逐层筛选多目标优化策略设计钴基高温合金流程[117]

图8 基于材料因子和Bayesian优化的沉淀硬化铜合金多目标设计策略[123]

图9 多目标转化为单目标优化的方法[124]

图10 Pareto前沿优化示意图

图11 多目标Bayesian优化框架[128]

图12 编码与解码设计流程[135]

图13 机器学习设计系统(MLDS)[140]

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