Download file (3.35 MB)
Download as zip (3.19 MB)

Collection

Citation

Description:

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.

Rights:

• Creative Commons BY-NC Attribution-NonCommercial 4.0 International
  

Keyword: cost-sensitive, iterative machine-learning method, small and imbalanced materials data sets, chemical knowledge, CSIML

Date published: 2024-05-02

Publisher: Oxford University Press (OUP)

Journal:

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

Funding:

• JSPS JP20H05883
• JSPS JP20H05878
• JST PRESTO JPMJPR20T4

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1093/chemle/upae090

Related item:

Other identifier(s):

Contact agent:

Updated at: 2024-11-28 16:30:28 +0900

Published on MDR: 2024-11-28 16:30:29 +0900