Yuyang Wang
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- Computational Drug Discovery Methods 7
- Materials Chemistry top 10%
- Machine Learning in Materials Science 8
- X-ray Diffraction in Crystallography 2
- Graphene research and applications 1
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- Protein Structure and Dynamics 6
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- Nanopore and Nanochannel Transport Studies 2
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- Forecasting Techniques and Applications 2
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- Cavitation Phenomena in Pumps 1
- Co-authors
- Amir Barati FarimaniZhonglin CaoJianren WangRishikesh MagarLiang ChenKenji ShimadaTim JanuschowskiPrakarsh Yadav
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Journal of the American Chemical Society (1 paper)The Journal of Chemical Physics (1 paper)
- Partner nations
- United StatesChinaSweden
In The Last Decade
Yuyang Wang
19 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 119
- Computational Theory and Mathematics 461
- Materials Chemistry 599
- Catalysis 55
- Metals and Alloys 16
- Inorganic Chemistry 65
Countries citing papers authored by Yuyang Wang
This map shows the geographic impact of Yuyang Wang'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 Yuyang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuyang Wang more than expected).
Fields of papers citing papers by Yuyang Wang
This network shows the impact of papers produced by Yuyang Wang. 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 Yuyang Wang. The network helps show where Yuyang Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yuyang Wang, 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 | 13 | |
| 2 | 2023 | 22 | |
| 3 | 2023 | 97 | |
| 4 | 2023 | 98 | |
| 5 | 2023 | 15 | |
| 6 | 2023 | 11 | |
| 7 | 2023 | 2 | |
| 8 | 2022 | 25 | |
| 9 | 2022 | 42 | |
| 10 | 2022 | 25 | |
| 11 | 2022 | 48 | |
| 12 | Molecular contrastive learning of representations via graph neural networksbreakdown → | 2022 | 466 |
| 13 | 2022 | 50 | |
| 14 | 2022 | 13 | |
| 15 | AugLiChem: Data Augmentation Library ofChemical Structures for Machine Learning. | 2021 | 1 |
| 16 | 2021 | 15 | |
| 17 | 2021 | 23 | |
| 18 | 2021 | 63 | |
| 19 | Deep Learning for Forecasting: Current Trends and Challenges | 2018 | 12 |
About Yuyang Wang
Yuyang Wang is a scholar working on Computational Theory and Mathematics, Process Chemistry and Technology and Computer Graphics and Computer-Aided Design, having authored 19 papers that have together received 1.0k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (7 papers), Protein Structure and Dynamics (6 papers), Nanopore and Nanochannel Transport Studies (2 papers), Forecasting Techniques and Applications (2 papers), X-ray Diffraction in Crystallography (2 papers), Cavitation Phenomena in Pumps (1 paper) and Graphene research and applications (1 paper). The work is most often cited by research in Computational Theory and Mathematics (461 citations), Materials Chemistry (599 citations) and Catalysis (55 citations). Yuyang Wang has collaborated with scholars based in United States, China and Sweden. Frequent co-authors include Amir Barati Farimani, Zhonglin Cao, Jianren Wang, Rishikesh Magar, Liang Chen, Kenji Shimada, Tim Januschowski, Prakarsh Yadav, Ali Caner Türkmen and Zijie Li. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and The Journal of 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.