An Su
Impact in
-
- Machine Learning in Materials Science
- Covalent Organic Framework Applications
-
- Metal-Organic Frameworks: Synthesis and Applications
Papers in
-
- Computational Drug Discovery Methods 10
-
- Machine Learning in Materials Science 16
- Co-authors
- Weike Su (15 shared papers)Abhijit N. Shirke (5 shared papers)Richard A. Gross (5 shared papers)Chengwei Zhang (8 shared papers)Hongliang Duan (12 shared papers)Chengwei Zhang (4 shared papers)Yun‐Fang Yang (6 shared papers)Yuanbin She (7 shared papers)
- Journals
- Industrial & Engineering Chemistry Research (3 papers)Chemical Engineering Science (3 papers)Organic Chemistry Frontiers (2 papers)Briefings in Bioinformatics (2 papers)Physical Chemistry Chemical Physics (2 papers)
- Partner nations
- ChinaMacaoUnited States
In The Last Decade
An Su
48 papers receiving 655 citations
Peers
Comparison fields: 5 of 85
- Materials Chemistry 259
- Inorganic Chemistry 77
- Pollution 62
- Biomaterials 68
- Computational Theory and Mathematics 86
Countries citing papers authored by An Su
This map shows the geographic impact of An Su'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 An Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites An Su more than expected).
Fields of papers citing papers by An Su
This network shows the impact of papers produced by An Su. 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 An Su. The network helps show where An Su may publish in the future.
Co-authors
The 25 scholars most cited alongside An Su, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 115 | |
| 2 | 2021 | 55 | |
| 3 | 2023 | 48 | |
| 4 | 2018 | 36 | |
| 5 | 2016 | 34 | |
| 6 | 2016 | 31 | |
| 7 | 2021 | 29 | |
| 8 | 2021 | 28 | |
| 9 | 2023 | 24 | |
| 10 | 2024 | 17 | |
| 11 | 2015 | 16 | |
| 12 | 2024 | 16 | |
| 13 | 2022 | 14 | |
| 14 | 2024 | 14 | |
| 15 | 2023 | 13 | |
| 16 | 2024 | 12 | |
| 17 | 2018 | 12 | |
| 18 | 2023 | 12 | |
| 19 | 2019 | 11 | |
| 20 | 2024 | 11 |
About An Su
An Su is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Inorganic Chemistry, Molecular Biology and Biomedical Engineering, having authored 52 papers that have together received 672 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (16 papers), Innovative Microfluidic and Catalytic Techniques Innovation (11 papers), Computational Drug Discovery Methods (10 papers), Chemical Synthesis and Analysis (6 papers), Asymmetric Hydrogenation and Catalysis (4 papers), Protein Structure and Dynamics (4 papers), Advanced Photocatalysis Techniques (4 papers) and Microfluidic and Capillary Electrophoresis Applications (4 papers). The work is most often cited by research in Materials Chemistry (259 citations), Inorganic Chemistry (77 citations), Pollution (62 citations), Biomaterials (68 citations) and Computational Theory and Mathematics (86 citations). An Su has collaborated with scholars based in China, Macao and United States. Frequent co-authors include Weike Su, Abhijit N. Shirke, Richard A. Gross, Chengwei Zhang, Hongliang Duan, Chengwei Zhang, Yun‐Fang Yang, Yuanbin She, Ke‐Jun Wu and Chengyun Zhang. Their work appears in journals such as Industrial & Engineering Chemistry Research, Chemical Engineering Science, Organic Chemistry Frontiers, Briefings in Bioinformatics and Physical Chemistry Chemical Physics.
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.