Peichen Zhong
- Materials Chemistry top 10%
- Machine Learning in Materials Science 16
- X-ray Diffraction in Crystallography 5
- Automotive Engineering top 10%
- Advanced Battery Technologies Research 2
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- Advancements in Battery Materials 10
- Advanced Battery Materials and Technologies 8
- Advanced Memory and Neural Computing 3
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- Extraction and Separation Processes 3
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- Inorganic Chemistry and Materials 2
Peichen Zhong
26 papers receiving 848 citations
Hit Papers
Peers
Comparison fields: 5 of 55
- Materials Chemistry 550
- Automotive Engineering 106
- Electrical and Electronic Engineering 400
- Catalysis 45
- Computational Theory and Mathematics 74
Countries citing papers authored by Peichen Zhong
This map shows the geographic impact of Peichen Zhong'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 Peichen Zhong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peichen Zhong more than expected).
Fields of papers citing papers by Peichen Zhong
This network shows the impact of papers produced by Peichen Zhong. 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 Peichen Zhong. The network helps show where Peichen Zhong may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Peichen Zhong, 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 | 1 | |
| 2 | 2025 | 7 | |
| 3 | Systematic softening in universal machine learning interatomic potentialsbreakdown → | 2025 | 51 |
| 4 | 2025 | 1 | |
| 5 | 2025 | 6 | |
| 6 | 2025 | 3 | |
| 7 | 2025 | 2 | |
| 8 | 2025 | 1 | |
| 9 | A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials | 2025 | 0 |
| 10 | 2025 | 1 | |
| 11 | 2024 | 18 | |
| 12 | 2024 | 14 | |
| 13 | 2024 | 9 | |
| 14 | 2023 | 19 | |
| 15 | CHGNet as a pretrained universal neural network potential for charge-informed atomistic modellingbreakdown → | 2023 | 444 |
| 16 | 2023 | 9 | |
| 17 | 2022 | 14 | |
| 18 | 2022 | 6 | |
| 19 | 2022 | 37 | |
| 20 | 2021 | 12 |
About Peichen Zhong
Peichen Zhong is a scholar working on Materials Chemistry, Electrical and Electronic Engineering, Inorganic Chemistry, Condensed Matter Physics and Automotive Engineering, having authored 27 papers that have together received 873 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (16 papers), Advancements in Battery Materials (10 papers), Advanced Battery Materials and Technologies (8 papers), X-ray Diffraction in Crystallography (5 papers), Advanced Memory and Neural Computing (3 papers), Extraction and Separation Processes (3 papers), Advanced Battery Technologies Research (2 papers) and Inorganic Chemistry and Materials (2 papers). The work is most often cited by research in Materials Chemistry (550 citations), Automotive Engineering (106 citations), Electrical and Electronic Engineering (400 citations), Catalysis (45 citations) and Computational Theory and Mathematics (74 citations). Peichen Zhong has collaborated with scholars based in United States, Austria and China. Frequent co-authors include Gerbrand Ceder, Bowen Deng, KyuJung Jun, Janosh Riebesell, Christopher J. Bartel, Kevin Han, Luis Barroso-Luque, Yang Ha, Mahalingam Balasubramanian and Wanli Yang. Their work appears in journals such as npj Computational Materials, Physical review. B., Chemistry of Materials, ACS Energy Letters and Advanced Energy Materials.
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.