How to design high-performance materials by mining the relationship between properties and structure features of materials is a major challenge today. We developed a new strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon materials (PCMs) using machine learning (ML) on the basis of hundreds of experimental data in the literature. Six ML models were selected to predict capacitance with the closely related structural features of PCMs. XGBoost demonstrates best predictive performance of supercapacitor (R = 0.892) among all ML models. The accurate predicted ability of the developed models could significantly reduce experiment workload for the assisted‐design of high-performance supercapacitor materials. Smicro/SSA, SSA, and PS provided more contribution to the capacitive performance among all porous structural features. The overall results of this study will provide a new idea for design high-performance materials by mining the relationship between properties and structure features of materials using an emerging ML strategy.

如何通过挖掘材料性能与结构特征之间的关系来设计高性能材料是当今的一大挑战。我们在文献中数百个实验数据的基础上，通过使用机器学习 (ML) 挖掘多孔碳材料 (PCM) 的电容与结构特征之间的关系，开发了一种辅助设计高性能超级电容器材料的新策略。选择了六个 ML 模型来预测与 PCM 密切相关的结构特征的电容。XGBoost 在所有 ML 模型中展示了超级电容器的最佳预测性能 (R = 0.892)。所开发模型的准确预测能力可以显着减少辅助设计高性能超级电容器材料的实验工作量。Smicro/SSA, SSA, 在所有多孔结构特征中，PS 对电容性能的贡献更大。本研究的总体结果将通过使用新兴的 ML 策略挖掘材料性能与结构特征之间的关系，为设计高性能材料提供新思路。