An accurate machine-learning calculator for optimization of Li-ion battery cathodes-Reference-Cited by-同舟云学术

An accurate machine-learning calculator for optimization of Li-ion battery cathodes

Published:2020-08-07 Issue:5 Volume:153 Page:054124
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Houchins Gregory¹^ORCID,Viswanathan Venkatasubramanian¹²^ORCID

Affiliation:

1. Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA

2. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA

Funder

XSEDE

National Science Foundation

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/5.0015872

Reference46 articles.

1. Performance Metrics Required of Next-Generation Batteries to Make a Practical Electric Semi Truck

2. Performance Metrics Required of Next-Generation Batteries to Electrify Commercial Aircraft

3. The Development and Future of Lithium Ion Batteries

4. Lithium-Ion Battery Supply Chain Considerations: Analysis of Potential Bottlenecks in Critical Metals

5. P. Faguy, “Next-generation lithium-ion batteries: Electrode architecture and cell materials research projects,” Report No. BAT337, Department of Energy, 2018.

Cited by 55 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. Predicting battery applications for complex materials based on chemical composition and machine learning;Computational Materials Science;2025-01

2. Study of structural, electric, and electrochemical properties and storage mechanism of Na doped LiFePO4 cathode materials;Physica B: Condensed Matter;2024-10

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

4. Electrification-driven circular economy with machine learning-based multi-scale and cross-scale modelling approach;Energy;2024-07

5. Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review;Journal of the American Ceramic Society;2024-06-09