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

Tao Wang, Runtong Pan, Murillo L. Martins, Jinlei Cui, Zhennan Huang, Bishnu P. Thapaliya, Chi Linh Do-Thanh, Musen Zhou, Juntian Fan, Zhenzhen Yang, Miaofang Chi, Takeshi Kobayashi, Jianzhong Wu, Eugene Mamontov, Sheng Dai*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number4607
JournalNature communications
Volume14
Issue number1
DOIs
StatePublished - Dec 2023

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy

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