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A Data Quality and Quantity Governance for Machine Learning in Materials Science
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Yue LIU1, 4, Shuchang MA1, Zhengwei YANG1, Xinxin ZOU1, Siqi SHI2, 3
Journal of the Chinese Ceramic Society | 2023, 51(2) : 427 - 437
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Journal of the Chinese Ceramic Society | 2023, 51(2): 427-437
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A Data Quality and Quantity Governance for Machine Learning in Materials Science
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Yue LIU1, 4, Shuchang MA1, Zhengwei YANG1, Xinxin ZOU1, Siqi SHI2, 3
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
  • LIU Yue (1975-), female, Ph.D., Professor. E-mail:

About Author:

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

Published: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220991
Outline
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Data-driven machine learning is widely used in materials property prediction and structure-activity relationship research due to its accurate and efficient predictive ability. Data determines the upper limit of machine learning. However, materials data often have various quality and quantity problems (i.e., multiple sources, large noise, small samples, and high dimensionality), affecting the application of machine learning in the materials field. In this paper, by analyzing the data quality and quantity problems and their related governance work, we find that data quality and data quantity jointly determine this problem. Following this, a data quality and quantity governance framework embedded by materials domain knowledge in the whole process of materials machine learning is proposed. We define twelve dimensions to analyze the connotation of materials data quality and quantity. A life cycle model of data quality and quantity governance is constructed to ensure that data quality and quantity governance activities are carried out in an orderly manner. To manage data quality and quantity accurately and comprehensively, a series of corresponding governance processing models are established from domain knowledge and data-driven aspects, which provides technical support for the specific implementation of the life cycle model. This framework realizes the overall evaluation and improvement of materials data quality and quantity, providing theoretical guidance and candidate solutions for high-quality and appropriate-quantity data acquisition and accelerating the in-depth application of machine learning in materials research and development.

materials science  /  machine learning  /  data quality and quantity  /  domain knowledge
刘悦, 马舒畅, 杨正伟, 邹欣欣, 施思齐. 面向材料领域机器学习的数据质量治理. 硅酸盐学报, 2023 , 51 (2) : 427 -437 . DOI: 10.14062/j.issn.0454-5648.20220991
Yue LIU, Shuchang MA, Zhengwei YANG, Xinxin ZOU, Siqi SHI. A Data Quality and Quantity Governance for Machine Learning in Materials Science[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 427 -437 . DOI: 10.14062/j.issn.0454-5648.20220991
Year 2023 volume 51 Issue 2
45
3
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Article Info
doi: 10.14062/j.issn.0454-5648.20220991
  • Receive Date:2022-11-18
  • Online Date:2026-10-20
  • Published:2023-02-20
Article Data
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History
  • Received:2022-11-18
  • Revised:2022-12-12
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

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
species (%)
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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