Jiang Wang

82 papers receiving 3.0k citations

Jiang Wang's Hit Papers

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields 2019 · 355 citations
3550+2+4Years since publication100200300

Peers

Jiang Wang
Comparison fields: 5 of 162
  • Finance 306
  • Analytical Chemistry 236
  • Materials Chemistry 1.1k
  • Computational Theory and Mathematics 298
  • Information Systems 346
Replace Sheng Liu with:
Sheng Liu China
Pedro Carpena Spain
Min Dai China
Jure Zupan Slovenia
Yumei Huang China
Panos Argyrakis Greece
Gordon Anderson United States
Ronald W. Rousseau United States
Xue Chen China
Hiroshi Ohta Japan
Jiang Wang relative to Sheng Liu China Sheng Liu's profile →
Citations per field
00.5×7.1×
Sheng Liu · 1×
Citations per year

Countries citing papers authored by Jiang Wang

Since Specialization
Citations

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

Fields of papers citing papers by Jiang Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jiang Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jiang Wang Line = papers co-authored together Jiang Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 85 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004384
2
Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
Hit paper breakdown →
2019355
3 2006238
4 2012170
5 2020122
6 2018117
7 2019113
8 2019100
9 200480
10 200980
11 201672
12 201370
13 200067
14 201467
15 201067
16 201353
17 201153
18 199549
19 199547
20 202143

About Jiang Wang

Jiang Wang is a scholar working on Materials Chemistry, Molecular Biology, Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics and Mechanical Engineering, having authored 85 papers that have together received 3.1k indexed citations. Recurring topics across this work include Hydrocarbon exploration and reservoir analysis (8 papers), Protein Structure and Dynamics (8 papers), Semiconductor Quantum Structures and Devices (7 papers), Shape Memory Alloy Transformations (7 papers), Machine Learning in Materials Science (6 papers), Financial Markets and Investment Strategies (5 papers), Phase Equilibria and Thermodynamics (4 papers) and Spectroscopy and Chemometric Analyses (4 papers). The work is most often cited by research in Finance (306 citations), Analytical Chemistry (236 citations), Materials Chemistry (1.1k citations), Computational Theory and Mathematics (298 citations) and Information Systems (346 citations). Jiang Wang has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Jiawei Han, Hüseyin Şehitoğlu, Andrew L. Ferguson, Leonid Kogan, Hans Jürgen Maier, Frank Noé, Cecilia Clementi, Mark M. Westerfield, Stephen A. Ross and Nicholas E. Charron. Their work appears in journals such as The Journal of Physical Chemistry B, The Journal of Chemical Physics, ACS Omega, International Journal of Plasticity and Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy.

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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