Hanyu Gao

2.5k citations
56 papers · 1.7k indexed · 1 hit paper · h-index 16
Topics
Machine Learning in Materials Science (21 papers)Computational Drug Discovery Methods (9 papers)Advanced Polymer Synthesis and Characterization (8 papers)

In The Last Decade

Hanyu Gao

48 papers receiving 1.7k citations

Hit Papers

A robotic platform for flow synthesis of organic compound...20192026202120232019200400600

Peers

Hanyu Gao
Comparison fields: 5 of 123
  • Materials Chemistry 907
  • Biomedical Engineering 511
  • Computational Theory and Mathematics 446
  • Molecular Biology 328
  • Organic Chemistry 227
Replace Milad Abolhasani with:
Milad Abolhasani United States
Pieter Plehiers Belgium
Zhonglin Cao United States
Marcio Schwaab Brazil
Florence H. Vermeire Belgium
Changwon Suh United States
Qi Shen China
Jason M. Stevens United States
Peng Gao China
Yang Su China
Hanyu Gao relative to Milad Abolhasani United States Milad Abolhasani's profile →
Citations per field
00.5×3.2×
Milad Abolhasani · 1×
Citations per year

Countries citing papers authored by Hanyu Gao

Since Specialization
Citations

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

Fields of papers citing papers by Hanyu Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hanyu Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Hanyu Gao. A scholar is included among the top collaborators of Hanyu Gao 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 Hanyu Gao. Hanyu Gao 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
#WorkIndexed citations
1 0
2 0
3 10
4 2
5 2
6 0
7 2
8 5
9 36
10 6
11 12
12 5
13 14
14 64
15 48
16 13
17
A robotic platform for flow synthesis of organic compounds informed by AI planningbreakdown →
730
18 291
19 21
20 1

About Hanyu Gao

Hanyu Gao is a scholar working on Materials Chemistry, Polymers and Plastics and Computational Theory and Mathematics, having authored 56 papers that have together received 1.7k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (21 papers), Computational Drug Discovery Methods (9 papers) and Advanced Polymer Synthesis and Characterization (8 papers). The work is most often cited by research in Computational Theory and Mathematics (446 citations), Materials Chemistry (907 citations) and Catalysis (91 citations). Hanyu Gao has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Connor W. Coley, Klavs F. Jensen, William H. Green, Thomas J. Struble, Yuran Wang, Pieter Plehiers, Travis Hart, Robert W. Hicklin, Victor Schultz and Timothy F. Jamison. Their work appears in journals such as Science, Angewandte Chemie International Edition and Nature Communications.

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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