Cheng Shang

4.1k total citations
89 papers, 3.2k citations indexed

About

Cheng Shang is a scholar working on Materials Chemistry, Catalysis and Renewable Energy, Sustainability and the Environment. According to data from OpenAlex, Cheng Shang has authored 89 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Materials Chemistry, 15 papers in Catalysis and 15 papers in Renewable Energy, Sustainability and the Environment. Recurrent topics in Cheng Shang's work include Machine Learning in Materials Science (36 papers), Catalytic Processes in Materials Science (19 papers) and Electrocatalysts for Energy Conversion (11 papers). Cheng Shang is often cited by papers focused on Machine Learning in Materials Science (36 papers), Catalytic Processes in Materials Science (19 papers) and Electrocatalysts for Energy Conversion (11 papers). Cheng Shang collaborates with scholars based in China, United States and Singapore. Cheng Shang's co-authors include Zhi‐Pan Liu, Pei‐Lin Kang, Sida Huang, Xiaojie Zhang, Qianyu Liu, Xiaotian Li, Lin Chen, Sicong Ma, Yunfei Shi and Shuhui Guan and has published in prestigious journals such as Journal of the American Chemical Society, Angewandte Chemie International Edition and Nature Communications.

In The Last Decade

Cheng Shang

83 papers receiving 3.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Cheng Shang China 30 2.4k 717 717 618 351 89 3.2k
Jess Wellendorff United States 9 1.8k 0.7× 845 1.2× 988 1.4× 636 1.0× 207 0.6× 13 2.6k
Nongnuch Artrith United States 25 2.5k 1.1× 350 0.5× 418 0.6× 1.0k 1.6× 127 0.4× 39 3.4k
Christopher J. Bartel United States 25 3.2k 1.4× 382 0.5× 446 0.6× 1.8k 2.9× 368 1.0× 59 4.2k
David D. Landis Denmark 6 1.5k 0.6× 542 0.8× 741 1.0× 539 0.9× 226 0.6× 7 2.0k
Mie Andersen Denmark 25 1.7k 0.7× 508 0.7× 675 0.9× 591 1.0× 86 0.2× 54 2.3k
Thomas W. Chamberlain United Kingdom 32 1.8k 0.8× 271 0.4× 311 0.4× 531 0.9× 301 0.9× 114 3.2k
Hyung‐Kyu Lim South Korea 34 1.3k 0.6× 432 0.6× 1.8k 2.5× 2.6k 4.3× 256 0.7× 115 4.5k
Scott Kirklin United States 17 4.4k 1.9× 313 0.4× 387 0.5× 1.6k 2.6× 665 1.9× 23 5.5k
Zijing Lin China 36 2.1k 0.9× 492 0.7× 379 0.5× 1.3k 2.1× 102 0.3× 165 3.5k
Philomena Schlexer Italy 19 1.7k 0.7× 1.1k 1.5× 1.5k 2.1× 580 0.9× 137 0.4× 37 2.8k

Countries citing papers authored by Cheng Shang

Since Specialization
Citations

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

Fields of papers citing papers by Cheng Shang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cheng Shang

This figure shows the co-authorship network connecting the top 25 collaborators of Cheng Shang. A scholar is included among the top collaborators of Cheng Shang 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 Cheng Shang. Cheng Shang 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.
Ding, Jieqiong, Peng Yao, Wei Xiong, et al.. (2025). Distinctly different active sites of ZnO-ZrO2 catalysts in CO2 and CO hydrogenation to methanol reactions. Nature Communications. 16(1). 4622–4622. 7 indexed citations
2.
Liu, Minchao, Zirui Lv, Peng Yao, et al.. (2025). Unlocking Advanced Architectures of Single‐Crystal Metal–Organic Frameworks. Angewandte Chemie International Edition. 64(14). e202423939–e202423939. 8 indexed citations
3.
Guan, Tong, et al.. (2025). Fine-Tuned Global Neural Network Potentials for Global Potential Energy Surface Exploration at High Accuracy. Journal of Chemical Theory and Computation. 21(7). 3576–3586. 2 indexed citations
4.
5.
Yang, Zhengxin, Yunfei Shi, Pei‐Lin Kang, et al.. (2024). LASP to the Future of Atomic Simulation: Intelligence and Automation. SHILAP Revista de lepidopterología. 2(12). 612–627. 14 indexed citations
6.
Guan, Tong, Cheng Shang, & Zhi‐Pan Liu. (2024). Local-Softening Stochastic Surface Walking for Fast Exploration of Corrugated Potential Energy Surfaces. Journal of Chemical Theory and Computation. 20(24). 11093–11104. 3 indexed citations
7.
Yang, Xiao, Cheng Shang, & Zhi‐Pan Liu. (2024). Generalized mechanism for the solid phase transition of M2O3 (M=AI, Ga) featuring single cation migration and martensitic lattice transformation. Chinese Journal of Chemical Physics. 37(4). 465–470.
8.
Shang, Cheng, et al.. (2024). Diffusion behaviors of lithium ions at the cathode/electrolyte interface from a global neural network potential. Journal of Materials Chemistry A. 12(48). 33808–33817. 1 indexed citations
9.
Liu, Qianyu, et al.. (2023). An optimal Fe–C coordination ensemble for hydrocarbon chain growth: a full Fischer–Tropsch synthesis mechanism from machine learning. Chemical Science. 14(35). 9461–9475. 15 indexed citations
10.
Wang, Chen, Xiyu Song, Yao Wang, et al.. (2023). A Solution‐Processable Porphyrin‐Based Hydrogen‐Bonded Organic Framework for Photoelectrochemical Sensing of Carbon Dioxide. Angewandte Chemie International Edition. 62(43). e202311482–e202311482. 43 indexed citations
11.
Shang, Cheng, et al.. (2023). Machine-learning atomic simulation for heterogeneous catalysis. npj Computational Materials. 9(1). 50 indexed citations
12.
Liu, Minchao, Cheng Shang, Tiancong Zhao, et al.. (2023). Site-specific anisotropic assembly of amorphous mesoporous subunits on crystalline metal–organic framework. Nature Communications. 14(1). 1211–1211. 44 indexed citations
13.
Shang, Cheng, et al.. (2022). Joint loss optimization based high similarity identification for milch goats. Journal of Image and Graphics. 27(4). 1137–1147. 4 indexed citations
14.
Guan, Shuhui, Cheng Shang, & Zhi‐Pan Liu. (2021). Structure and Dynamics of Energy Materials from Machine Learning Simulations: A Topical Review†. Chinese Journal of Chemistry. 39(11). 3144–3154. 11 indexed citations
15.
Ma, Sicong, Cheng Shang, Chuanming Wang, & Zhi‐Pan Liu. (2020). Thermodynamic rules for zeolite formation from machine learning based global optimization. Chemical Science. 11(37). 10113–10118. 33 indexed citations
16.
Kang, Pei‐Lin, Cheng Shang, & Zhi‐Pan Liu. (2020). Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration. Accounts of Chemical Research. 53(10). 2119–2129. 97 indexed citations
17.
Xia, Z. C., Minchen Wei, Bo Chen, et al.. (2016). 3D spin-flop transition in enhanced 2D layered structure single crystalline TlCo2Se2. Journal of Physics Condensed Matter. 28(39). 396002–396002. 2 indexed citations
18.
Shang, Cheng, Weina Zhao, & Zhi‐Pan Liu. (2015). Searching for new TiO2crystal phases with better photoactivity. Journal of Physics Condensed Matter. 27(13). 134203–134203. 21 indexed citations
19.
Zhu, Yihan, Jiating He, Cheng Shang, et al.. (2014). Chiral Gold Nanowires with Boerdijk–Coxeter–Bernal Structure. Journal of the American Chemical Society. 136(36). 12746–12752. 76 indexed citations
20.
Li, Zhijun, et al.. (2012). Research of coordinated control method of hybrid power crane system. International Conference on Modelling, Identification and Control. 1093–1097. 4 indexed citations

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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