Exploring low-cost high energy NASICON cathodes for sodium-ion batteries via a combined machine-learning, ab initio, and experimental approach

Author:

Soundharrajan Vaiyapuri1,Alfaruqi Muhammad Hilmy12ORCID,Alfaza Ghalib1,Lee Jun1,Lee Seulgi1,Park Sohyun1,Nithiananth Subramanian3,Pham Duong Tung4,Hwang Jang-Yeon5ORCID,Kim Jaekook16ORCID

Affiliation:

1. Department of Materials Science and Engineering, Chonnam National University, 300 Yongbong-dong, Bukgu, Gwangju 500-757, South Korea

2. Department of Metallurgical Engineering, Sumbawa University of Technology, Olat Maras, Sumbawa, West Nusa Tenggara, 84371, Indonesia

3. Graduate School of Science and Technology, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu, Shizuoka, 432-8011, Japan

4. School of Engineering Physics, Hanoi University of Science and Technology, No 1 Dai Co Viet Street, 100000, Hanoi, Viet Nam

5. Department of Energy Engineering, Hanyang University, Seoul 133-791, South Korea

6. Research Center for Artificial Intelligence Assisted Ionics Based Materials Development Platform, Chonnam National University, Gwangju, 61186, Republic of Korea

Abstract

This study demonstrated the fusion of machine-learning, ab initio, and experimental approaches model to develop new NASICON type cathodes including Na3.5MnV0.5Ti0.5(PO4)3, Na3.5MnV0.5Fe0.5(PO4)3, and Na3.5MnV0.5Al0.5(PO4)3 for SIBs.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

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