Data‐Driven Design of NASICON‐Type Electrodes Using Graph‐Based Neural Networks Article Swipe
Yoonsu Shim
,
Incheol Jeong
,
Junpyo Hur
,
Hyoungjeen Jeen
,
Seung‐Taek Myung
,
Kang Taek Lee
,
Seungbum Hong
,
Jong Min Yuk
,
Chan‐Woo Lee
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/batt.202400186
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/batt.202400186
Sodium superionic conductor (NASICON)‐type cathode materials are considered promising candidates for high‐performance sodium‐ion batteries (SIBs) because of the abundance and low cost of raw materials. However, NASICON‐type cathodes suffer from low capacities. This limitation can be addressed through the activation of sodium‐excess phases, which can enhance capacities up to theoretical values. Thus, this paper proposes the use of transition metal (TM)‐substituted Na 3 V 2 (PO 4 ) 2 F 3 (NVPF) to induce sodium‐excess phases. To identify suitable doping elements, an inverse design approach is developed, combining machine learning prediction and density functional theory (DFT) calculations. Graph‐based neural networks are used to predict two crucial properties, i. e., the structural stability and voltage level. Results indicate that the use of TM‐substituted NVPF materials leads to about 150 % capacity enhancement with reduced time and resource requirements compared with the direct design approach. Furthermore, DFT calculations confirm improvements in cyclability, electronic conductivity, and chemical stability. The proposed approach is expected to accelerate the discovery of superior materials for battery electrodes.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/batt.202400186
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/batt.202400186
- OA Status
- hybrid
- Cited By
- 4
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394881552
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394881552Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/batt.202400186Digital Object Identifier
- Title
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Data‐Driven Design of NASICON‐Type Electrodes Using Graph‐Based Neural NetworksWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-04-17Full publication date if available
- Authors
-
Yoonsu Shim, Incheol Jeong, Junpyo Hur, Hyoungjeen Jeen, Seung‐Taek Myung, Kang Taek Lee, Seungbum Hong, Jong Min Yuk, Chan‐Woo LeeList of authors in order
- Landing page
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https://doi.org/10.1002/batt.202400186Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/batt.202400186Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/batt.202400186Direct OA link when available
- Concepts
-
Fast ion conductor, Artificial neural network, Computer science, Electrode, Graph, Materials science, Artificial intelligence, Chemistry, Theoretical computer science, Electrolyte, Physical chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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