引用本文:
Shisheng Zheng, Haowen Ding, Shunning Li, Dong Chen, Feng PanShisheng Zheng, Haowen Ding, Shunning Li, Dong Chen, Feng Pan. Application of topology-based structure feature for machine learning in material science[J]. Chinese Journal of Structural Chemistry,
2023, 42(7): 100120.
doi:
10.1016/j.cjsc.2023.100120
Citation: Shisheng Zheng, Haowen Ding, Shunning Li, Dong Chen, Feng PanShisheng Zheng, Haowen Ding, Shunning Li, Dong Chen, Feng Pan. Application of topology-based structure feature for machine learning in material science[J]. Chinese Journal of Structural Chemistry, 2023, 42(7): 100120. doi: 10.1016/j.cjsc.2023.100120
Citation: Shisheng Zheng, Haowen Ding, Shunning Li, Dong Chen, Feng PanShisheng Zheng, Haowen Ding, Shunning Li, Dong Chen, Feng Pan. Application of topology-based structure feature for machine learning in material science[J]. Chinese Journal of Structural Chemistry, 2023, 42(7): 100120. doi: 10.1016/j.cjsc.2023.100120
Application of topology-based structure feature for machine learning in material science
摘要:
Structure features play an important role in machine learning models for the materials investigation. Here, two topology-based features for the representation of material structure, specifically structure graph and algebraic topology, are introduced. We present the fundamental mathematical concepts underlying these techniques and how they encode material properties. Furthermore, we discuss the practical applications and enhancements of these feature made in specific material predicting tasks. This review may provide suggestions on the selection of suitable structural features and inspire creativity in developing robust descriptors for diverse applications.
English
Application of topology-based structure feature for machine learning in material science
Abstract:
Structure features play an important role in machine learning models for the materials investigation. Here, two topology-based features for the representation of material structure, specifically structure graph and algebraic topology, are introduced. We present the fundamental mathematical concepts underlying these techniques and how they encode material properties. Furthermore, we discuss the practical applications and enhancements of these feature made in specific material predicting tasks. This review may provide suggestions on the selection of suitable structural features and inspire creativity in developing robust descriptors for diverse applications.
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Key words:
- Machine learning
- / Structure feature
- / Structure graph
- / Algebraic topology
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