Machine learning interatomic potentials in engineering perspective for developing cathode materials-Reference-Cited by-同舟云学术

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Machine learning interatomic potentials in engineering perspective for developing cathode materials

Published:2024 Issue:35 Volume:12 Page:23837-23847
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Kwon Dohyeong¹^ORCID,Kim Duho¹²^ORCID

Affiliation:

1. Department of Mechanical Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Republic of Korea

2. Department of KHU-KIST Convergence Science and Technology, Kyung Hee University, 23, Kyunghee-daero Dongdaemun-gu, Seoul 02447, Republic of Korea

Abstract

Machine learning interatomic potentials (MLIPs) predict thermodynamic phase stability and structural parameters like density functional theory (DFT) but are much faster, making them valuable for engineering applications.

Funder

Kyung Hee University

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D4TA03452J

Reference72 articles.

1. LixCoO2 (0

2. Recent advances and historical developments of high voltage lithium cobalt oxide materials for rechargeable Li-ion batteries

3. Improvement of float charge durability for LiCoO2 electrodes under high voltage and storage temperature by suppressing O1-Phase transition

4. Challenges for Rechargeable Li Batteries

5. Nonaqueous Liquid Electrolytes for Lithium-Based Rechargeable Batteries

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