Fujie Tang

1.6k total citations · 1 hit paper
29 papers, 1.2k citations indexed

About

Fujie Tang is a scholar working on Atomic and Molecular Physics, and Optics, Spectroscopy and Atmospheric Science. According to data from OpenAlex, Fujie Tang has authored 29 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Atomic and Molecular Physics, and Optics, 7 papers in Spectroscopy and 4 papers in Atmospheric Science. Recurrent topics in Fujie Tang's work include Spectroscopy and Quantum Chemical Studies (19 papers), Quantum, superfluid, helium dynamics (9 papers) and Advanced Chemical Physics Studies (7 papers). Fujie Tang is often cited by papers focused on Spectroscopy and Quantum Chemical Studies (19 papers), Quantum, superfluid, helium dynamics (9 papers) and Advanced Chemical Physics Studies (7 papers). Fujie Tang collaborates with scholars based in China, United States and Germany. Fujie Tang's co-authors include Mischa Bonn, Yuki Nagata, Tatsuhiko Ohto, Ellen H. G. Backus, Thomas D. Kühne, Renato Torre, Fivos Perakis, Luigi De Marco, Andrey Shalit and Zachary R. Kann and has published in prestigious journals such as Chemical Reviews, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Fujie Tang

27 papers receiving 1.1k citations

Hit Papers

Vibrational Spectroscopy and Dynamics of Water 2016 2026 2019 2022 2016 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fujie Tang China 13 720 276 276 165 126 29 1.2k
Lawrence F. Scatena United States 9 770 1.1× 213 0.8× 244 0.9× 130 0.8× 213 1.7× 18 1.3k
Quan Du China 4 851 1.2× 153 0.6× 252 0.9× 145 0.9× 179 1.4× 9 1.2k
Gao‐Lei Hou China 21 527 0.7× 473 1.7× 196 0.7× 213 1.3× 94 0.7× 109 1.5k
Yuen Ron Shen United States 7 835 1.2× 145 0.5× 290 1.1× 133 0.8× 103 0.8× 14 1.1k
Ondřej Maršálek Czechia 22 1.1k 1.5× 447 1.6× 254 0.9× 127 0.8× 135 1.1× 43 1.7k
Marco Masia Italy 21 866 1.2× 245 0.9× 272 1.0× 95 0.6× 127 1.0× 50 1.4k
Daniel Spångberg Sweden 22 810 1.1× 530 1.9× 229 0.8× 89 0.5× 133 1.1× 40 1.6k
Daria Ruth Galimberti France 18 541 0.8× 177 0.6× 227 0.8× 60 0.4× 116 0.9× 35 924
Tomas K. Hirsch Sweden 7 872 1.2× 374 1.4× 242 0.9× 198 1.2× 207 1.6× 9 1.5k
Niklas Ottosson Sweden 23 953 1.3× 272 1.0× 267 1.0× 186 1.1× 133 1.1× 45 1.6k

Countries citing papers authored by Fujie Tang

Since Specialization
Citations

This map shows the geographic impact of Fujie Tang'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 Fujie Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fujie Tang more than expected).

Fields of papers citing papers by Fujie Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fujie Tang. 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 Fujie Tang. The network helps show where Fujie Tang may publish in the future.

Co-authorship network of co-authors of Fujie Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Fujie Tang. A scholar is included among the top collaborators of Fujie Tang 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 Fujie Tang. Fujie Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Guo, Wentao, Feng Wang, Hongshuai Wang, et al.. (2025). Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts. Nature Computational Science. 5(4). 292–300. 9 indexed citations
2.
Wang, Yongkang, Fujie Tang, Xiaoqing Yu, et al.. (2025). Interfaces govern the structure of angstrom-scale confined water solutions. Nature Communications. 16(1). 7288–7288. 3 indexed citations
3.
Tang, Fujie, Diana Y. Qiu, & Xifan Wu. (2025). Optical Absorption Spectroscopy Probes Water Wire and Its Ordering in a Hydrogen-Bond Network. Physical Review X. 15(1). 1 indexed citations
5.
Wang, Yongkang, Fujie Tang, Xiaoqing Yu, et al.. (2024). Heterodyne‐Detected Sum‐Frequency Generation Vibrational Spectroscopy Reveals Aqueous Molecular Structure at the Suspended Graphene/Water Interface. Angewandte Chemie International Edition. 63(20). e202319503–e202319503. 9 indexed citations
6.
Wang, Junling, et al.. (2024). Batch Processing for Enhanced InISAR Imaging of Space Targets. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–15.
7.
Bao, Chenglong, et al.. (2024). Revealing the molecular structures of α-Al2O3(0001)–water interface by machine learning based computational vibrational spectroscopy. The Journal of Chemical Physics. 161(12). 8 indexed citations
8.
Tang, Fujie, et al.. (2023). Exploring the impact of ions on oxygen K-edge X-ray absorption spectroscopy in NaCl solution using the GW-Bethe-Salpeter-equation approach. The Journal of Chemical Physics. 159(17). 3 indexed citations
9.
Tang, Fujie, Zhenglu Li, Chunyi Zhang, et al.. (2022). Many-body effects in the X-ray absorption spectra of liquid water. Proceedings of the National Academy of Sciences. 119(20). e2201258119–e2201258119. 29 indexed citations
10.
Zhang, Chunyi, Fujie Tang, Mohan Chen, et al.. (2021). Modeling Liquid Water by Climbing up Jacob’s Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials. The Journal of Physical Chemistry B. 125(41). 11444–11456. 63 indexed citations
11.
Tang, Fujie, Xuanyuan Jiang, Hsin-Yu Ko, et al.. (2020). Probing ferroelectricity by X-ray absorption spectroscopy in molecular crystals. Insecta mundi. 4 indexed citations
12.
Zhang, Chunyi, Linfeng Zhang, Jianhang Xu, et al.. (2020). Isotope effects in x-ray absorption spectra of liquid water. Physical review. B.. 102(11). 6 indexed citations
13.
Tang, Fujie, Tatsuhiko Ohto, Shumei Sun, et al.. (2020). Molecular Structure and Modeling of Water–Air and Ice–Air Interfaces Monitored by Sum-Frequency Generation. Chemical Reviews. 120(8). 3633–3667. 141 indexed citations
14.
Ohto, Tatsuhiko, Jianhang Xu, Sho Imoto, et al.. (2019). Accessing the Accuracy of Density Functional Theory through Structure and Dynamics of the Water–Air Interface. The Journal of Physical Chemistry Letters. 10(17). 4914–4919. 55 indexed citations
16.
Zhang, Ruidan, Ting Luo, Fujie Tang, et al.. (2019). Adsorption Structure and Coverage-Dependent Orientation Analysis of Sub-Monolayer Acetonitrile on TiO2(110). The Journal of Physical Chemistry C. 123(29). 17915–17924. 8 indexed citations
17.
Sun, Shumei, Fujie Tang, Sho Imoto, et al.. (2018). Orientational Distribution of Free O-H Groups of Interfacial Water is Exponential. Physical Review Letters. 121(24). 246101–246101. 60 indexed citations
18.
Smit, Wilbert J., Fujie Tang, M. Alejandra Sánchez, et al.. (2017). Excess Hydrogen Bond at the Ice-Vapor Interface around 200 K. Physical Review Letters. 119(13). 133003–133003. 48 indexed citations
19.
Hosseinpour, Saman, Fujie Tang, Fenglong Wang, et al.. (2017). Chemisorbed and Physisorbed Water at the TiO2/Water Interface. The Journal of Physical Chemistry Letters. 8(10). 2195–2199. 101 indexed citations
20.
Perakis, Fivos, Luigi De Marco, Andrey Shalit, et al.. (2016). Vibrational Spectroscopy and Dynamics of Water. Chemical Reviews. 116(13). 7590–7607. 346 indexed citations breakdown →

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.

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