Machine Learning-assisted Discovery of Multifunctional Coordination in Multicomponent Composites

Zi-Ran Guo; Sen Xue; Lu He; Zi-Long Xie; Tian-Hao Yang; Qiang Fu

doi:10.1007/s10118-025-3479-3

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Machine Learning-assisted Discovery of Multifunctional Coordination in Multicomponent Composites

RESEARCH ARTICLE | Updated：2026-01-13

- Machine Learning-assisted Discovery of Multifunctional Coordination in Multicomponent Composites
- Chinese Journal of Polymer Science Vol. 44, Issue 1, Pages: 256-267(2026)
- Affiliations：
  
  College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China
- Author bio：
  
  qiangfu@scu.edu.cn
- Funds：
- DOI：10.1007/s10118-025-3479-3
  CLC：
- Received：12 September 2025，
  
  Revised：2025-09-25，
  
  Accepted：11 October 2025，
  
  Online First：18 December 2025，
  
  Published：15 January 2026
- Accepted：
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Guo, Z. R.; Xue, S.; He, L.; Xie, Z. L.; Yang, T. H.; Fu, Q. Machine learning-assisted discovery of multifunctional coordination in multicomponent composites. Chinese J. Polym. Sci. 2026, 44, 256–267

Zi-Ran Guo, Sen Xue, Lu He, et al. Machine Learning-assisted Discovery of Multifunctional Coordination in Multicomponent Composites[J]. Chinese Journal of Polymer Science, 2026, 44(1): 256-267.
Guo, Z. R.; Xue, S.; He, L.; Xie, Z. L.; Yang, T. H.; Fu, Q. Machine learning-assisted discovery of multifunctional coordination in multicomponent composites. Chinese J. Polym. Sci. 2026, 44, 256–267 DOI： 10.1007/s10118-025-3479-3.

Zi-Ran Guo, Sen Xue, Lu He, et al. Machine Learning-assisted Discovery of Multifunctional Coordination in Multicomponent Composites[J]. Chinese Journal of Polymer Science, 2026, 44(1): 256-267. DOI： 10.1007/s10118-025-3479-3.

摘要

Machine learning-assisted formulation optimization of multicomponent composites offers an efficient and effective alternative to traditional trial-and-error experiment.

Abstract

The complex interactions and conflicting performance demands in multi-component composites pose significant challenges for achieving balanced multi-property optimization through conventional trial-and-error approaches. Machine learning (ML) offers a promising solution

markedly improving materials discovery efficiency. However

the high dimensionality of feature spaces in such systems has long impeded effective ML-driven feature representation and inverse design. To overcome this

we present an Intelligent Screening System (ISS) framework to accelerate the discovery of optimal formulations balancing four key properties in 15-component PTFE-based copper-clad laminate composites (PTFE-CCLCs). ISS adopts modular descriptors based on the physical information of component volume fractions

thereby simplifying the feature representation. By leveraging the inverse prediction capability of ML models and constructing a performance-driven virtual candidate database

ISS significantly reduced the computational complexity associated with high-dimensional spaces. Experimental validation confirmed that ISS-optimized formulations exhibited superior synergy

notably resolving the trade-off between thermal conductivity and peel strength

and outperform many commercial counterparts. Despite limited data and inherent process variability

ISS achieved an average prediction accuracy of 76.5%

with thermal conductivity predictions exceeding 90%

demonstrating robust reliability. This work provides an innovative

efficient strategy for multifunctional optimization and accelerated discovery in ultra-complex composite systems

highlighting the integration of ML and advanced materials design.

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references

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