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<p>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.</p>

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Machine Learning-assisted Discovery of Multifunctional Coordination in Multicomponent Composites

  • 期刊论文

  • 发表日期：

    2025年12月18日

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

摘要

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.