Sheng Gong
About
In The Last Decade
Sheng Gong
45 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Electrical and Electronic Engineering 1.6k
- Materials Chemistry 849
- Electronic, Optical and Magnetic Materials 473
- Automotive Engineering 262
- Inorganic Chemistry 168
Countries citing papers authored by Sheng Gong
This map shows the geographic impact of Sheng Gong'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 Sheng Gong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sheng Gong more than expected).
Fields of papers citing papers by Sheng Gong
This network shows the impact of papers produced by Sheng Gong. 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 Sheng Gong. The network helps show where Sheng Gong may publish in the future.
Co-authorship network of co-authors of Sheng Gong
This figure shows the co-authorship network connecting the top 25 collaborators of Sheng Gong. A scholar is included among the top collaborators of Sheng Gong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sheng Gong. Sheng Gong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | A predictive machine learning force-field framework for liquid electrolyte development breakdown → | 29 |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 9 | |
| 9 | 59 | |
| 10 | 4 | |
| 11 | 6 | |
| 12 | 12 | |
| 13 | 6 | |
| 14 | 2 | |
| 15 | 7 | |
| 16 | Classifying superheavy elements by machine learning | 1 |
| 17 | 6 | |
| 18 | 7 | |
| 19 | 61 | |
| 20 | 2011 IEEE 11th International Conference on Data Mining | 6 |
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.