Guohao Li

4.0k total citations · 3 hit papers
65 papers, 2.1k citations indexed

About

Guohao Li is a scholar working on Materials Chemistry, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Guohao Li has authored 65 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Materials Chemistry, 19 papers in Artificial Intelligence and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Guohao Li's work include MXene and MAX Phase Materials (15 papers), Advancements in Battery Materials (8 papers) and Advanced Graph Neural Networks (7 papers). Guohao Li is often cited by papers focused on MXene and MAX Phase Materials (15 papers), Advancements in Battery Materials (8 papers) and Advanced Graph Neural Networks (7 papers). Guohao Li collaborates with scholars based in China, Saudi Arabia and United States. Guohao Li's co-authors include Bernard Ghanem, Ali Thabet, Matthias Müller, Guocheng Qian, Xiuqiang Xie, Nan Zhang, Fei Song, Zhenjun Wu, Jie Wang and Matthias Mueller and has published in prestigious journals such as Angewandte Chemie International Edition, SHILAP Revista de lepidopterología and ACS Nano.

In The Last Decade

Guohao Li

55 papers receiving 2.1k citations

Hit Papers

DeepGCNs: Can GCNs Go As Deep As CNNs? 2019 2026 2021 2023 2019 2021 2024 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guohao Li China 18 643 577 532 387 313 65 2.1k
Liang Pan China 28 210 0.3× 927 1.6× 302 0.6× 790 2.0× 552 1.8× 125 2.8k
Chengjie Wang China 34 1.6k 2.4× 2.4k 4.2× 131 0.2× 176 0.5× 191 0.6× 144 4.0k
Ruimao Zhang China 19 533 0.8× 1.2k 2.0× 336 0.6× 133 0.3× 276 0.9× 72 2.1k
Zhe Liu China 26 291 0.5× 971 1.7× 63 0.1× 332 0.9× 169 0.5× 216 2.6k
Feng Chen China 26 531 0.8× 674 1.2× 418 0.8× 685 1.8× 113 0.4× 144 2.8k
Zhibo Chen China 40 796 1.2× 4.2k 7.4× 320 0.6× 900 2.3× 141 0.5× 245 6.5k
Lin Wang China 23 337 0.5× 554 1.0× 341 0.6× 373 1.0× 67 0.2× 164 1.9k
Chaoyue Wang China 22 367 0.6× 1.1k 1.8× 190 0.4× 222 0.6× 84 0.3× 79 2.2k
Qiuping Jiang China 34 412 0.6× 3.3k 5.8× 108 0.2× 261 0.7× 99 0.3× 211 4.4k
Shuai Li China 26 304 0.5× 1.2k 2.1× 118 0.2× 129 0.3× 152 0.5× 132 2.1k

Countries citing papers authored by Guohao Li

Since Specialization
Citations

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

Fields of papers citing papers by Guohao Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guohao Li

This figure shows the co-authorship network connecting the top 25 collaborators of Guohao Li. A scholar is included among the top collaborators of Guohao Li 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 Guohao Li. Guohao Li 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.
Li, Guohao, et al.. (2025). Superior piezoelectric performance in high- T C sodium–bismuth titanate ferroelectric ceramics through spark plasma sintering. Journal of Materials Chemistry C. 13(18). 9115–9123.
2.
3.
Chen, Yu, et al.. (2024). The effect of temperature on mechanical properties of calcium silicate hydrate enhanced by graphene oxide and graphene via molecular simulation study. Materials Today Communications. 40. 109836–109836. 1 indexed citations
4.
Cheng, Yayi, Fangli Yu, Yingying Zhou, et al.. (2024). Chemically bonded MXene/SnSe2 composite with special structural transformation as a high-performance anode for lithium and potassium ions battery. Chemical Engineering Journal. 481. 148737–148737. 34 indexed citations
5.
Li, Guohao, Jie Wang, Haotian Wu, et al.. (2024). 3D isotropic MXene films enabling minimized polarization for enhanced sodium-ion storage performance. Energy storage materials. 68. 103327–103327. 9 indexed citations
6.
Alshehri, Abdulelah S., et al.. (2024). Leveraging 2D molecular graph pretraining for improved 3D conformer generation with graph neural networks. Computers & Chemical Engineering. 183. 108622–108622. 6 indexed citations
7.
Wang, Kun, Xinnan Zhang, Guohao Li, et al.. (2024). The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs. 3164–3175. 3 indexed citations
8.
Qiu, Jianing, Kyle Lam, Guohao Li, et al.. (2024). LLM-based agentic systems in medicine and healthcare. Nature Machine Intelligence. 6(12). 1418–1420. 48 indexed citations breakdown →
9.
Wang, Kun, Guohao Li, Kai Wang, et al.. (2024). The Snowflake Hypothesis: Training and Powering GNN with One Node One Receptive Field. 3152–3163. 3 indexed citations
11.
Li, Wenzhong, et al.. (2023). Multi-Domain Generalized Graph Meta Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 4479–4487. 7 indexed citations
12.
Yu, Weijiang, et al.. (2023). Knowledge-aware Global Reasoning for Situation Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(7). 1–13. 6 indexed citations
13.
Wang, Kun, Yuxuan Liang, Xinglin Li, et al.. (2023). Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(5). 3388–3405. 11 indexed citations
14.
Li, Guohao, et al.. (2023). DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(11). 1–12. 14 indexed citations
15.
Li, Guohao, Zuxuan Wu, Chen Zhu, et al.. (2022). Robust Optimization as Data Augmentation for Large-scale Graphs. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 60–69. 50 indexed citations
16.
Li, Guohao, Brian C. Wyatt, Fei Song, et al.. (2021). 2D Titanium Carbide (MXene) Based Films: Expanding the Frontier of Functional Film Materials. Advanced Functional Materials. 31(46). 117 indexed citations
17.
Li, Guohao, et al.. (2021). DeepGCNs: Making GCNs Go as Deep as CNNs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(6). 6923–6939. 130 indexed citations
18.
Zhao, Shuoqing, Zhichao Liu, Guanshun Xie, et al.. (2021). Achieving High‐Performance 3D K+‐Pre‐intercalated Ti3C2Tx MXene for Potassium‐Ion Hybrid Capacitors via Regulating Electrolyte Solvation Structure. Angewandte Chemie. 133(50). 26450–26457. 3 indexed citations
19.
Zhao, Shuoqing, Zhichao Liu, Guanshun Xie, et al.. (2021). Achieving High‐Performance 3D K+‐Pre‐intercalated Ti3C2Tx MXene for Potassium‐Ion Hybrid Capacitors via Regulating Electrolyte Solvation Structure. Angewandte Chemie International Edition. 60(50). 26246–26253. 72 indexed citations
20.
Li, Guohao, Matthias Müller, Ali Thabet, & Bernard Ghanem. (2019). Can GCNs Go as Deep as CNNs. arXiv (Cornell University). 22 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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