Machine-Learning-Driven Advanced Characterization of Battery Electrodes-Reference-Cited by-同舟云学术

Machine-Learning-Driven Advanced Characterization of Battery Electrodes

Published:2022-11-09 Issue:12 Volume:7 Page:4368-4378
ISSN:2380-8195
Container-title:ACS Energy Letters
language:en
Short-container-title:ACS Energy Lett.

Author:

Finegan Donal P.¹^ORCID,Squires Isaac²,Dahari Amir²,Kench Steve²^ORCID,Jungjohann Katherine L.¹^ORCID,Cooper Samuel J.²

Affiliation:

1. National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States

2. Dyson School of Design Engineering, Imperial College London, London SW7 2DB, U.K.

Funder

Henry Royce Institute

U.S. Department of Energy

Faraday Institution

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment,Chemistry (miscellaneous)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsenergylett.2c01996

Reference69 articles.

1. Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence

2. Artificial Intelligence Applied to Battery Research: Hype or Reality?

3. Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

4. Spatial dynamics of lithiation and lithium plating during high-rate operation of graphite electrodes

5. Time-Resolved X-ray Operando Observations of Lithiation Gradients across the Cathode Matrix and Individual Oxide Particles during Fast Cycling of a Li-Ion Cell

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