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This research proposes a methodology using explainable AI to optimize the design of carbon composites with low filler loading for enhanced thermal conductivity. By employing high-throughput finite element simulations and symbolic regression, the study identifies key factors influencing thermal conductivity, achieving predictive accuracy over 99%. The findings demonstrate that graphene-based composites can achieve high thermal conductivities exceeding 33 W m−1 K−1 with less than 10 wt% carbon fillers, offeri
- Author
- edificehx301
- Language
- EN