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<p>Sugar-derived carbon fibers (SBCFs) emerge as a promising next-generation sustainable material due to their biomass origin, cost-effectiveness, and superior strength-to-weight ratio. However, industrial adoption remains hindered by inefficient optimization of complex pre-carbonization processes. Here, we present a machine learning (ML)-driven framework to address this challenge, integrating experimental data to establish quantitative correlations between pre-carbonization parameters (temperature, dwell time) and mechanical performance. Gradient Boosted Decision Trees (GBDT) achieved superior predictive accuracy (<i>R</i><sup>2</sup> = 0.857 for tensile strength), enabling efficient identification of optimal conditions: 220&#xa0;°C pre-carbonization temperature with 100&#xa0;min dwell time. Experimental validation confirmed a 5.60% tensile strength enhancement over baseline protocols. Optimized protocols yield fibers with 874&#xa0;MPa tensile strength and 76 GPa modulus, with an average diameter of 28&#xa0;μm. This machine learning-driven methodology not only advances SBCF manufacturing but also establishes a generalizable paradigm for accelerating functional material development.</p>

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Machine learning-optimized pre-carbonization process for sugar-based carbon fibers

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Wenping Cao,
  • Jiabin Tu,
  • Wanxiaonan Chen,
  • Linsen Zhang,
  • Qianru Lin,
  • Jie Sheng

摘要

Sugar-derived carbon fibers (SBCFs) emerge as a promising next-generation sustainable material due to their biomass origin, cost-effectiveness, and superior strength-to-weight ratio. However, industrial adoption remains hindered by inefficient optimization of complex pre-carbonization processes. Here, we present a machine learning (ML)-driven framework to address this challenge, integrating experimental data to establish quantitative correlations between pre-carbonization parameters (temperature, dwell time) and mechanical performance. Gradient Boosted Decision Trees (GBDT) achieved superior predictive accuracy (R2 = 0.857 for tensile strength), enabling efficient identification of optimal conditions: 220 °C pre-carbonization temperature with 100 min dwell time. Experimental validation confirmed a 5.60% tensile strength enhancement over baseline protocols. Optimized protocols yield fibers with 874 MPa tensile strength and 76 GPa modulus, with an average diameter of 28 μm. This machine learning-driven methodology not only advances SBCF manufacturing but also establishes a generalizable paradigm for accelerating functional material development.