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Machine Learning Embedded with Materials Domain Knowledge
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Yue LIU1, 4, 5, Xinxin ZOU1, Zhengwei YANG1, Siqi SHI2, 3, 5
Journal of the Chinese Ceramic Society | 2022, 50(3) : 863 - 876
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Journal of the Chinese Ceramic Society | 2022, 50(3): 863-876
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Machine Learning Embedded with Materials Domain Knowledge
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Yue LIU1, 4, 5, Xinxin ZOU1, Zhengwei YANG1, Siqi SHI2, 3, 5
Affiliations
  • 1.School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
  • 2.School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
  • 3.Materials Genome Institute, Shanghai University, Shanghai 200444, China
  • 4.Shanghai Engineering Research Center of Intelligent Computing System, Shanghai 200444, China
  • 5.Zhejiang Laboratory, Hangzhou 311100, China

    • LIU Yue (1975-), female, Ph.D., Professor. E-mail:

About Author:

LIU Yue (1975-), female, Ph.D., Professor. E-mail:

Published: 2022-03-20 doi: 10.14062/j.issn.0454-5648.20220093
Outline
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Data-driven Machine Learning (ML) has been widely used in materials performance optimization and novel materials design due to its ability to quickly fit potential data patterns and achieve accurate prediction. However, the results of data-driven ML are often inconsistent with the materials basic theory or principle, which results mainly from the lack of the guidance of materials domain knowledge, e.g., the correlation among descriptors and the driving mechanism associated with the properties. Herein, by analyzing the characteristics of materials data and the modeling principle of data-driven ML methods, we clarify the three main contradictions occurring to the application of ML in materials science, i.e., the contradictions between high dimension and small sample, complexity and accuracy of models, learning results and domain knowledge. Following this, we propose the ML method embedded with materials domain knowledge to reconcile these three contradictions. Further, surrounding the whole ML process including target definition, data collection and preprocessing, feature engineering, model construction and application, we explore some key techniques to realize domain knowledge embedding by summarizing the related basic and exploratory efforts. Finally, opportunities and challenges facing the ML method embedded with domain knowledge are also discussed.

materials design  /  machine learning  /  materials data
刘悦, 邹欣欣, 杨正伟, 施思齐. 材料领域知识嵌入的机器学习. 硅酸盐学报, 2022 , 50 (3) : 863 -876 . DOI: 10.14062/j.issn.0454-5648.20220093
Yue LIU, Xinxin ZOU, Zhengwei YANG, Siqi SHI. Machine Learning Embedded with Materials Domain Knowledge[J]. Journal of the Chinese Ceramic Society, 2022 , 50 (3) : 863 -876 . DOI: 10.14062/j.issn.0454-5648.20220093
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Year 2022 volume 50 Issue 3
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Article Info
doi: 10.14062/j.issn.0454-5648.20220093
  • Receive Date：2022-01-30
  • Online Date：2026-10-22
  • Published：2022-03-20
Article Data
Affiliations
History
  • Received：2022-01-30
  • Revised：2022-02-15
Funding
Affiliations
    1.School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    2.School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
    3.Materials Genome Institute, Shanghai University, Shanghai 200444, China
    4.Shanghai Engineering Research Center of Intelligent Computing System, Shanghai 200444, China
    5.Zhejiang Laboratory, Hangzhou 311100, China

Corresponding:

SHI Siqi (1978-), male, Ph.D., Professor. E-mail:
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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