Junqiang Wang

9.4k citations
322 papers · 7.5k · 1 hit paper · h-index 42

Impact in

Papers in

Junqiang Wang

305 papers receiving 7.3k citations

Junqiang Wang's Hit Papers

Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model 2019 · 397 citations
3970+2+4Years since publication100200300

Peers

Junqiang Wang
Comparison fields: 5 of 172
  • Ceramics and Composites 739
  • Mechanical Engineering 3.0k
  • Materials Chemistry 2.9k
  • Electronic, Optical and Magnetic Materials 1.1k
  • Renewable Energy, Sustainability and the Environment 675
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Ning Li China
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Citations per field
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Citations per year

Countries citing papers authored by Junqiang Wang

Since Specialization
Citations

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

Fields of papers citing papers by Junqiang Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Junqiang 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 Junqiang Wang Line = papers co-authored together Junqiang Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 322 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model
Hit paper breakdown →
2019397
2 2009385
3 2012285
4 2011144
5 2012140
6 2015121
7 2014115
8 2017113
9 2014111
10 2015109
11 2014108
12 2017108
13 2020107
14 2015103
15 201787
16 201784
17 201982
18 201879
19 201374
20 202072

About Junqiang Wang

Junqiang Wang is a scholar working on Ceramics and Composites, Mechanical Engineering, Materials Chemistry, Electronic, Optical and Magnetic Materials and Renewable Energy, Sustainability and the Environment, having authored 322 papers that have together received 7.5k indexed citations. Recurring topics across this work include Metallic Glasses and Amorphous Alloys (85 papers), Material Dynamics and Properties (46 papers), Glass properties and applications (42 papers), Phase-change materials and chalcogenides (36 papers), Electrocatalysts for Energy Conversion (23 papers), High Entropy Alloys Studies (17 papers), Nanoporous metals and alloys (17 papers) and Magnetic and transport properties of perovskites and related materials (16 papers). The work is most often cited by research in Ceramics and Composites (739 citations), Mechanical Engineering (3.0k citations), Materials Chemistry (2.9k citations), Electronic, Optical and Magnetic Materials (1.1k citations) and Renewable Energy, Sustainability and the Environment (675 citations). Junqiang Wang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Juntao Huo, John H. Perepezko, Dmitri Valentinovich Louzguine, Mingwei W. Chen, Xinmin Wang, Akihisa Inoue, Weihua Wang, Lijian Song, Run-Wei Li and Yufeng Zheng. Their work appears in journals such as Journal of Alloys and Compounds, Journal of Non-Crystalline Solids, Intermetallics, Science China Physics Mechanics and Astronomy and Applied Physics Letters.

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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