KyuJung Jun
- Materials Chemistry top 10%
- Machine Learning in Materials Science 4
- Solid-state spectroscopy and crystallography 3
- Nuclear Materials and Properties 3
- Automotive Engineering top 5%
- Advanced Battery Technologies Research 4
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- Advanced Battery Materials and Technologies 15
- Advancements in Battery Materials 15
- Inorganic Chemistry top 10%
- Zeolite Catalysis and Synthesis 3
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- Extraction and Separation Processes 2
- Co-authors
- Gerbrand CederPeichen ZhongBowen DengJanosh RiebesellChristopher J. BartelKevin HanYihan XiaoLincoln J. Miara
- Journals
- Advanced Energy Materials (4 papers)Chemistry of Materials (3 papers)Nature Machine Intelligence (2 papers)
- Partner nations
- United StatesSouth KoreaUnited Kingdom
In The Last Decade
KyuJung Jun
22 papers receiving 941 citations
Hit Papers
Peers
Comparison fields: 5 of 55
- Materials Chemistry 641
- Automotive Engineering 128
- Electrical and Electronic Engineering 508
- Catalysis 46
- Inorganic Chemistry 86
Countries citing papers authored by KyuJung Jun
This map shows the geographic impact of KyuJung Jun's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by KyuJung Jun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites KyuJung Jun more than expected).
Fields of papers citing papers by KyuJung Jun
This network shows the impact of papers produced by KyuJung Jun. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by KyuJung Jun. The network helps show where KyuJung Jun may publish in the future.
Co-authorship network
The 25 scholars most cited alongside KyuJung Jun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 7 | |
| 2 | 2025 | 3 | |
| 3 | 2025 | 1 | |
| 4 | 2025 | 0 | |
| 5 | Systematic softening in universal machine learning interatomic potentialsbreakdown → | 2025 | 51 |
| 6 | 2025 | 0 | |
| 7 | 2025 | 1 | |
| 8 | 2025 | 8 | |
| 9 | 2024 | 14 | |
| 10 | 2024 | 23 | |
| 11 | 2024 | 4 | |
| 12 | 2024 | 58 | |
| 13 | 2024 | 14 | |
| 14 | 2023 | 20 | |
| 15 | 2023 | 21 | |
| 16 | 2023 | 20 | |
| 17 | 2023 | 10 | |
| 18 | 2022 | 163 | |
| 19 | 2021 | 61 | |
| 20 | 2019 | 6 |
About KyuJung Jun
KyuJung Jun is a scholar working on Inorganic Chemistry, Automotive Engineering, Electrical and Electronic Engineering, Materials Chemistry and Condensed Matter Physics, having authored 24 papers that have together received 967 indexed citations. Recurring topics across this work include Advanced Battery Materials and Technologies (15 papers), Advancements in Battery Materials (15 papers), Machine Learning in Materials Science (4 papers), Advanced Battery Technologies Research (4 papers), Solid-state spectroscopy and crystallography (3 papers), Zeolite Catalysis and Synthesis (3 papers), Nuclear Materials and Properties (3 papers) and Extraction and Separation Processes (2 papers). The work is most often cited by research in Materials Chemistry (641 citations), Automotive Engineering (128 citations), Electrical and Electronic Engineering (508 citations), Catalysis (46 citations) and Inorganic Chemistry (86 citations). KyuJung Jun has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Gerbrand Ceder, Peichen Zhong, Bowen Deng, Janosh Riebesell, Christopher J. Bartel, Kevin Han, Yihan Xiao, Lincoln J. Miara, Yan Wang and Haegyeom Kim. Their work appears in journals such as Advanced Energy Materials, Chemistry of Materials, Nature Machine Intelligence, Science Advances and Proceedings of the National Academy of Sciences.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.