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

doi:10.1038/s41467-023-40282-1

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

Research output: Contribution to journal › Article › peer-review

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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 language	English
Article number	4607
Journal	Nature Communications
Volume	14
Issue number	1
DOIs	https://doi.org/10.1038/s41467-023-40282-1
State	Published - Dec 2023

Funding

This work was supported as part of the Fluid Interface Reactions, Structures and Transport (FIRST) Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences at Oak Ridge National Laboratory under contract # DE-AC05–00OR22725. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research under the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). M.L.M. acknowledges partial support from NSF (award CHE-2102425) for working on the manuscript. This work was supported as part of the Fluid Interface Reactions, Structures and Transport (FIRST) Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences at Oak Ridge National Laboratory under contract # DE-AC05–00OR22725. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research under the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). M.L.M. acknowledges partial support from NSF (award CHE-2102425) for working on the manuscript.

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Wang, T., Pan, R., Martins, M. L., Cui, J., Huang, Z., Thapaliya, B. P., Do-Thanh, C. L., Zhou, M., Fan, J., Yang, Z., Chi, M., Kobayashi, T., Wu, J., Mamontov, E., & Dai, S. (2023). Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors. Nature Communications, 14(1), Article 4607. https://doi.org/10.1038/s41467-023-40282-1

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

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.",

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

note = "Publisher Copyright: {\textcopyright} 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.",

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doi = "10.1038/s41467-023-40282-1",

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Wang, T, Pan, R, Martins, ML, Cui, J, Huang, Z, Thapaliya, BP, Do-Thanh, CL, Zhou, M, Fan, J , Yang, Z , Chi, M, Kobayashi, T, Wu, J, Mamontov, E & Dai, S 2023, 'Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors', Nature Communications, vol. 14, no. 1, 4607. https://doi.org/10.1038/s41467-023-40282-1

TY - JOUR

T1 - Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors

AU - Wang, Tao

AU - Pan, Runtong

AU - Martins, Murillo L.

AU - Cui, Jinlei

AU - Huang, Zhennan

AU - Thapaliya, Bishnu P.

AU - Do-Thanh, Chi Linh

AU - Zhou, Musen

AU - Fan, Juntian

AU - Yang, Zhenzhen

AU - Chi, Miaofang

AU - Kobayashi, Takeshi

AU - Wu, Jianzhong

AU - Mamontov, Eugene

AU - Dai, Sheng

PY - 2023/12

Y1 - 2023/12

N2 - 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.

AB - 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.

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

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