Xiaobo Ji
- Materials Chemistry top 10%
- Machine Learning in Materials Science 11
- Layered Double Hydroxides Synthesis and Applications 4
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- Fuel Cells and Related Materials 5
- Gas Sensing Nanomaterials and Sensors 3
- Perovskite Materials and Applications 3
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- Computational Drug Discovery Methods 3
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- Distributed and Parallel Computing Systems 3
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- Conducting polymers and applications 3
- Journals
- The Journal of Physical Chemistry B (3 papers)npj Computational Materials (2 papers)Materials & Design (1 paper)
- Partner nations
- ChinaUnited Kingdom
In The Last Decade
Xiaobo Ji
32 papers receiving 779 citations
Hit Papers
Peers
Comparison fields: 5 of 119
- Materials Chemistry 397
- Metals and Alloys 15
- Electrical and Electronic Engineering 240
- Renewable Energy, Sustainability and the Environment 66
- Catalysis 28
Countries citing papers authored by Xiaobo Ji
This map shows the geographic impact of Xiaobo Ji'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 Xiaobo Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaobo Ji more than expected).
Fields of papers citing papers by Xiaobo Ji
This network shows the impact of papers produced by Xiaobo Ji. 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 Xiaobo Ji. The network helps show where Xiaobo Ji may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xiaobo Ji, 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 | 2024 | 15 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 6 | |
| 5 | 2023 | 34 | |
| 6 | 2023 | 21 | |
| 7 | Small data machine learning in materials sciencebreakdown → | 2023 | 395 |
| 8 | 2023 | 8 | |
| 9 | 2021 | 7 | |
| 10 | 2021 | 3 | |
| 11 | 2021 | 5 | |
| 12 | 2017 | 21 | |
| 13 | 2016 | 6 | |
| 14 | 2012 | 4 | |
| 15 | 2009 | 8 | |
| 16 | Dynamic fault-tolerance service framework for grid | 2008 | 1 |
| 17 | 2008 | 5 | |
| 18 | 2008 | 21 | |
| 19 | 2008 | 0 | |
| 20 | 2007 | 43 |
About Xiaobo Ji
Xiaobo Ji is a scholar working on Materials Chemistry, Hardware and Architecture and Toxicology, having authored 33 papers that have together received 806 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (11 papers), Fuel Cells and Related Materials (5 papers), Layered Double Hydroxides Synthesis and Applications (4 papers), Gas Sensing Nanomaterials and Sensors (3 papers), Computational Drug Discovery Methods (3 papers), Distributed and Parallel Computing Systems (3 papers), Perovskite Materials and Applications (3 papers) and Conducting polymers and applications (3 papers). The work is most often cited by research in Materials Chemistry (397 citations), Metals and Alloys (15 citations) and Electrical and Electronic Engineering (240 citations). Xiaobo Ji has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Wencong Lu, Minjie Li, Pengcheng Xu, Liuming Yan, Liang Liu, Baohua Yue, Junya Wang, Qing Zhang, Pan Xiong and Hongjie Zhang. Their work appears in journals such as The Journal of Physical Chemistry B, npj Computational Materials, Materials & Design, Science of Advanced Materials and The Journal of Physical Chemistry C.
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.