Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors - read online free

Aug 3, 2026

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Porous carbons are key active materials for aqueous supercapacitors, but improving physicochemical and electrochemical performance usually relies on time-consuming, costly experiments. This work applies machine learning to identify critical features and reports a machine-learning-derived activation strategy using sodium amide and cross-linked polymer precursors. The method synthesizes highly porous, oxygen-rich carbons with surface area >4000 m2/g and demonstrates a porous carbon electrode reaching 610 F/g in 1 M H2SO4, supported by mechanistic and transport studies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Porous carbons and aqueous supercapacitors

Limits of experimental development

Machine-learning approach

Identifying critical features

Predicted performance targets

Materials design and synthesis

Machine-learning-derived activation strategy

Sodium amide and cross-linked polymer precursors

Electrochemical performance

Specific capacitance and electrode loading

Oxygen content and pore tuning

Mechanism and characterization

Charge storage and electrolyte transport

UC Riverside UC Riverside Previously Published Works Title Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors Permalink [https://escholarship.org/uc/item/6933n19p](https://escholarship.org/uc/item/6933n19p) Journal Nature Communications, 14(1) ISSN 2041-1723 Authors Wang, Tao Pan, Runtong Martins, Murillo Let al. Publication Date 2023 DOI 10.1038/s41467-023-40282-1 Copyright Information This work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California Article [https://doi.org/10.1038/s41467-023-40282-1](https://doi.org/10.1038/s41467-023-40282-1) Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors Received: 23 June 2022 | Accepted: 18 July 2023 | | --- | | | | Check for updates | TaoWang 1,2, Runtong Pan 3, Murillo L. Martins4,7, Jinlei Cui5, Zhennan Huang6, Bishnu P. Thapaliya1,2, Chi-Linh Do-Thanh 2, Musen Zhou 3, Juntian Fan2, Zhenzhen Yang1,2, Miaofang Chi 6, Takeshi Kobayashi5, Jianzhong Wu 3, Eugene Mamontov 4 & Sheng Dai 1,2 Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to ﬁnd the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with speciﬁc surface areas > 4000 m2/g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm2 of electrode mass loading that exhibits a high speciﬁc capacitance of 610 F/g in 1 M H2SO4. This result approaches the speciﬁc capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements. Aqueous supercapacitors are critical energy storage devices for applications that require high power density and long cycle lifetime, such as regenerative braking systems in electric vehicles, uninterruptible power supplies, and power levelers for electronics1–4. With the fast development of supercapacitors, diverse materials including porous carbons, metal oxides/carbides/nitrides, and conductive polymers have been optimized to pursue a higher energy density in supercapacitors, among which porous carbons are still the primary and widely used active materials for commercial aqueous supercapacitors3,5–9. The advantages of porous carbons for supercapacitors include power capability, long-term cycle stability, wide operating temperatures, and high Coulombic efﬁciencies10–13. The basic energy storage mechanism of carbon supercapacitors is through an electrical double-layer capacitance (EDLC), derived from the reversible charge separation at the interface of the electrolyte with the carbon surface14,15. The large surface area and appropriate pore 1Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. 2Department of Chemistry, Institute for Advanced Materials and Manufacturing, University of Tennessee, Knoxville,

FAQs

Why are porous carbons important for aqueous supercapacitors?

Porous carbons deliver power capability, long-term cycle stability, and wide operating temperatures, making them widely used active materials in commercial aqueous supercapacitors.

How does machine learning contribute in this study?

Machine learning is used as a data-driven approach to extract critical features from previously reported research and guide the development of carbon materials with improved properties.

What activation strategy produces the high-performance porous carbon?

The study reports a machine-learning-derived activation strategy using sodium amide and cross-linked polymer precursors to synthesize oxygen-rich highly porous carbons with very high specific surface area.

Aug 3, 2026

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