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<p>The growing global demand for efficient energy storage has highlighted the need to discover and establish relationships and rules that link the electric double-layer (EDL) capacitance to the structural properties of the materials used in electrodes. Among these materials, nanoporous carbon stands out for its high microporosity and precisely adjustable pore size distribution, both of which play a crucial role in influencing EDL performance. At the same time, machine learning (ML) has emerged as a toolbox in materials science, enabling the prediction and optimization of various application-related properties based on experiment-derived structure and surface characteristics. In this study, the ML method was applied to 67 nanoporous carbon materials (carbide-derived carbons (CDCs)), with different structures and textures. Their EDL capacitance was then modeled under both positive and negative polarization in an electrolyte whose ions had an asymmetric, <i>i.e</i>., extremely non-spherical geometric structure. The structural and textural properties of the materials in the dataset were described using experimental descriptors of carbon materials derived from nitrogen and carbon dioxide adsorption measurements. These descriptors were used as model inputs, while the target property, EDL capacitance, was measured in three-electrode cells using 1.9&#xa0;M EMIm-TFSI in ACN as electrolyte. The ML modeling results demonstrate that combining experimentally derived structural descriptors, such as specific surface area, ion size-related volume fraction of pore size distribution, and bulk density of CDC electrodes, in one quantitative nanostructure–property relationship (QnSPR), enables accurate prediction of specific volumetric capacitance for both volumetric cathodic capacitance (R<sup>2</sup> = 0.93) and volumetric anodic capacitance (R<sup>2</sup> = 0.94) using multiple linear regression. The textural descriptors in the models indicate that the most effective pore size range for electrosorption is consistent with the smallest dimension of the ions, which is 0.4–0.5 nm to accommodate the EMIm<sup>+</sup> cation, while for the slightly larger but highly asymmetric TFSI<sup>−</sup> anion, it is below 0.4 nm.</p> Graphical abstract <p></p>

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Machine learning-assisted QnSPR study of structurally diverse nanoporous carbon materials and their capacitive behavior in dilute ionic liquid electrolyte

  • 期刊论文

  • 发表日期：

    2026年7月10日

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  • Maike Käärik,
  • Mati Arulepp,
  • Uko Maran,
  • Jaan Leis

摘要

The growing global demand for efficient energy storage has highlighted the need to discover and establish relationships and rules that link the electric double-layer (EDL) capacitance to the structural properties of the materials used in electrodes. Among these materials, nanoporous carbon stands out for its high microporosity and precisely adjustable pore size distribution, both of which play a crucial role in influencing EDL performance. At the same time, machine learning (ML) has emerged as a toolbox in materials science, enabling the prediction and optimization of various application-related properties based on experiment-derived structure and surface characteristics. In this study, the ML method was applied to 67 nanoporous carbon materials (carbide-derived carbons (CDCs)), with different structures and textures. Their EDL capacitance was then modeled under both positive and negative polarization in an electrolyte whose ions had an asymmetric, i.e., extremely non-spherical geometric structure. The structural and textural properties of the materials in the dataset were described using experimental descriptors of carbon materials derived from nitrogen and carbon dioxide adsorption measurements. These descriptors were used as model inputs, while the target property, EDL capacitance, was measured in three-electrode cells using 1.9 M EMIm-TFSI in ACN as electrolyte. The ML modeling results demonstrate that combining experimentally derived structural descriptors, such as specific surface area, ion size-related volume fraction of pore size distribution, and bulk density of CDC electrodes, in one quantitative nanostructure–property relationship (QnSPR), enables accurate prediction of specific volumetric capacitance for both volumetric cathodic capacitance (R2 = 0.93) and volumetric anodic capacitance (R2 = 0.94) using multiple linear regression. The textural descriptors in the models indicate that the most effective pore size range for electrosorption is consistent with the smallest dimension of the ions, which is 0.4–0.5 nm to accommodate the EMIm+ cation, while for the slightly larger but highly asymmetric TFSI− anion, it is below 0.4 nm.

Graphical abstract