Insights from machine learning of carbon electrodes for electric double layer capacitors

Zhou, Musen; Gallegos, Alejandro; Liu, Kun; Dai, Sheng; Wu, Jianzhong

doi:10.1016/j.carbon.2019.08.090

Title: Insights from machine learning of carbon electrodes for electric double layer capacitors

Journal Article · 31 January 2020 · Carbon

DOI: https://doi.org/10.1016/j.carbon.2019.08.090 · OSTI ID:1761737

^[1]; Gallegos, Alejandro ^[1]; Liu, Kun ^[1];

^[2];

^[1]

Univ. of California, Riverside, CA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Recent years have witnessed the broad use of carbon electrodes for electric double layer capacitors (EDLCs) because of large surface area, high porosity and low cost. Whereas experimental investigations are mostly focused on the device performance, computational studies have been rarely concerned with electrochemical properties at conditions remote from equilibrium, limiting their direct applications to materials design. Through a comprehensive analysis of extensive experimental data with various machine-learning methods, we report herein quantitative correlations between the structural features of carbon electrodes and the in-operando behavior of EDLCs including energy and power density. Machine learning allows us to identify important characteristics of activated carbons useful to optimize their efficiency in energy storage.

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Research Organization:: Energy Frontier Research Centers (EFRC) (United States). Fluid Interface Reactions, Structures and Transport Center (FIRST); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: AC02-05CH11231; AC05-00OR22725

OSTI ID:: 1761737

Journal Information:: Carbon, Journal Name: Carbon Journal Issue: n/a Vol. 157; ISSN 0008-6223

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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