Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale-Reference-Cited by-同舟云学术

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Published:2021-08-20 Issue:46 Volume:60 Page:24354-24366
ISSN:1433-7851
Container-title:Angewandte Chemie International Edition
language:en
Short-container-title:Angew. Chem. Int. Ed.

Author:

Chen Xiang¹^ORCID,Liu Xinyan¹²^ORCID,Shen Xin¹^ORCID,Zhang Qiang¹^ORCID

Affiliation:

1. Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing 100084 China

2. Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 611731 Sichuan China

Funder

National Natural Science Foundation of China

Beijing Municipal Natural Science Foundation

Publisher

Wiley

Subject

General Chemistry,Catalysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/anie.202107369

Reference131 articles.

1. Building better batteries

2. Towards greener and more sustainable batteries for electrical energy storage

3. Before Li Ion Batteries

4. Reviving the lithium metal anode for high-energy batteries

5. A review of flexible lithium–sulfur and analogous alkali metal–chalcogen rechargeable batteries

Cited by 106 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. Accelerating the design of catalysts for CO2 electroreduction to HCOOH: A data-driven DFT-ML screening of dual atom catalysts;Journal of Energy Chemistry;2024-12

2. Unveiling spin magnetic effect of TM doped SnS2 nanosheet with enhanced hydrogen evolution reaction;Journal of Energy Chemistry;2024-12

3. A high-flash-point quasi-solid polymer electrolyte for stable nickel-rich lithium metal batteries;Journal of Energy Chemistry;2024-12

4. Rational design of MXene-based vacancy-confined single-atom catalyst for efficient oxygen evolution reaction;Journal of Energy Chemistry;2024-11

5. Recognition of Molecular Structure of Phosphonium Salts from the Visual Appearance of Material with Deep Learning Can Reveal Subtle Homologs;Small;2024-09-10