ORIGINAL RESEARCH article
Front. Mater.
Sec. Carbon-Based Materials
Volume 12 - 2025 | doi: 10.3389/fmats.2025.1610601
Absorbent material composition prediction based on multi-objective regression with value stacking and selection
Provisionally accepted
- 1School of Computer Engineering, Jimei University, Xiamen, Fujian Province, China
- 2China Electronics Technology Group Corporation 33th Research Institute, Shanxi, China
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Electromagnetic wave absorption materials reduce incoming wave energy, with machine learning focusing on data-driven design methods. Traditional multi-objective regression methods often fail to provide accurate component predictions, limiting their performance. We propose a multiobjective predictive model for absorbent compositions. Using single-variable predictions as cumulative features in a regression chain improves feature representation. Performance metrics identify the optimal predictor variables for material composition, aiding in the classification of carbon nanotubes based on required performance and predicted values. Experimental results indicate that the model achieves better R 2 and MSE for carbon nanotubes, carbon black, and carbon fiber than other methods, with optimal ACC and MCC in classifying carbon nanotubes, validating the method for material composition design.
Keywords: Electromagnetic Wave Absorption Material, carbon nanotube, Multi-object Regression, material classification, GBDT
Received: 12 Apr 2025; Accepted: 18 Jun 2025.
Copyright: © 2025 He, Chen, Huang, Jian, Li and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Kai Huang, School of Computer Engineering, Jimei University, Xiamen, 361021, Fujian Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.