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説明:

Materials science research benefits from the powerful machine-learning (ML) surrogate models, but it is also limited by the implicit requirement for sufficiently big and balanced data distribution for ML. In this paper, we propose a model to obtain more credible results for small and imbalanced materials data sets as well as chemical knowledge. Taking 2 bandgaps imbalanced data sets as instances, we demonstrate the usability and performance of our model compared with common ML models with normal sampling and resampling methods.

権利情報:

• Creative Commons BY-NC Attribution-NonCommercial 4.0 International
  

キーワード: cost-sensitive, iterative machine-learning method, small and imbalanced materials data sets, chemical knowledge, CSIML

刊行年月日: 2024-05-02

出版者: Oxford University Press (OUP)

掲載誌:

• Chemistry Letters (ISSN: 03667022) vol. 53 issue. 5

研究助成金:

• JSPS JP20H05883
• JSPS JP20H05878
• JST PRESTO JPMJPR20T4

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1093/chemle/upae090

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更新時刻: 2024-11-28 16:30:28 +0900

MDRでの公開時刻: 2024-11-28 16:30:29 +0900