Feng Cheng

42 papers receiving 3.3k citations

Feng Cheng's Hit Papers

Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy 2019 · 460 citations
4600+2+5Years since publication200400600

Peers

Feng Cheng
Comparison fields: 5 of 114
  • Electronic, Optical and Magnetic Materials 1.1k
  • Acoustics and Ultrasonics 45
  • Developmental Biology 82
  • Renewable Energy, Sustainability and the Environment 521
  • Analytical Chemistry 274
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Countries citing papers authored by Feng Cheng

Since Specialization
Citations

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

Fields of papers citing papers by Feng Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials
Hit paper breakdown →
2018741
2
Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy
Hit paper breakdown →
2019460
3 2016329
4 2011295
5 2011250
6 2021115
7 201385
8 201183
9 201477
10 202077
11 201274
12 201572
13 202067
14 202167
15 201961
16 201957
17 201857
18 201657
19 200947
20 201937

About Feng Cheng

Feng Cheng is a scholar working on Materials Chemistry, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment, Electronic, Optical and Magnetic Materials and Organic Chemistry, having authored 43 papers that have together received 3.4k indexed citations. Recurring topics across this work include Advanced Photocatalysis Techniques (9 papers), Metamaterials and Metasurfaces Applications (7 papers), Electrocatalysts for Energy Conversion (6 papers), Advanced Battery Materials and Technologies (5 papers), Metal-Organic Frameworks: Synthesis and Applications (4 papers), Catalytic Processes in Materials Science (4 papers), Advancements in Battery Materials (4 papers) and Nanomaterials for catalytic reactions (4 papers). The work is most often cited by research in Electronic, Optical and Magnetic Materials (1.1k citations), Acoustics and Ultrasonics (45 citations), Developmental Biology (82 citations), Renewable Energy, Sustainability and the Environment (521 citations) and Analytical Chemistry (274 citations). Feng Cheng has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yongmin Liu, Wei Ma, Chun Wang, Qiuhua Wu, Yihao Xu, Zhi Wang, Qinlong Wen, Zuojia Wang, Shutao Gao and Ningzhao Shang. Their work appears in journals such as RSC Advances, Advanced Optical Materials, Energy Technology, Catalysis Communications and Applied Catalysis B: Environmental.

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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