Machine-Learning Approach for Predicting the Discharging Capacities of Doped Lithium Nickel–Cobalt–Manganese Cathode Materials in Li-Ion Batteries-Reference-Cited by-同舟云学术

Machine-Learning Approach for Predicting the Discharging Capacities of Doped Lithium Nickel–Cobalt–Manganese Cathode Materials in Li-Ion Batteries

Published:2021-08-20 Issue:9 Volume:7 Page:1551-1560
ISSN:2374-7943
Container-title:ACS Central Science
language:en
Short-container-title:ACS Cent. Sci.

Author:

Wang Guanyu¹^ORCID,Fearn Tom²^ORCID,Wang Tengyao²,Choy Kwang-Leong¹^ORCID

Affiliation:

1. Institute for Materials Discovery, Faculty of Maths and Physical Sciences, University College London, Roberts Building, London WC1E 7JE, United Kingdom

2. Department of Statistical Science, University College London, 1-19 Torrington Place, London WC1R 7HB, United Kingdom

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acscentsci.1c00611

Reference40 articles.

1. A reflection on lithium-ion battery cathode chemistry

2. A review of Ni-based layered oxides for rechargeable Li-ion batteries

3. Review—Recent Advances and Remaining Challenges for Lithium Ion Battery Cathodes

4. Layered Cathode Materials for Lithium-Ion Batteries: Review of Computational Studies on LiNi1–x–yCoxMnyO2 and LiNi1–x–yCoxAlyO2

5. Characterization of LiNi0.85Co0.10M0.05O2 (M = Al, Fe) as a cathode material for lithium secondary batteries

Cited by 44 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron + Bayesian optimization, ensemble learning, and CNN-LSTM models;Case Studies in Chemical and Environmental Engineering;2024-12

2. Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics;Toxicology;2024-11

3. ProADD: Proactive battery anomaly dual detection leveraging denoising convolutional autoencoder and incremental voltage analysis;Applied Energy;2024-11

4. A comprehensive data network for data-driven study of battery materials;Science and Technology of Advanced Materials;2024-09-11

5. Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries;Energy Storage Materials;2024-09