Zichen Song

489 total citations
3 papers, 10 citations indexed

About

Zichen Song is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Mechanical Engineering. According to data from OpenAlex, Zichen Song has authored 3 papers receiving a total of 10 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Materials Chemistry, 2 papers in Computational Theory and Mathematics and 1 paper in Mechanical Engineering. Recurrent topics in Zichen Song's work include X-ray Diffraction in Crystallography (2 papers), Computational Drug Discovery Methods (2 papers) and Machine Learning in Materials Science (2 papers). Zichen Song is often cited by papers focused on X-ray Diffraction in Crystallography (2 papers), Computational Drug Discovery Methods (2 papers) and Machine Learning in Materials Science (2 papers). Zichen Song collaborates with scholars based in United States, China and Hong Kong. Zichen Song's co-authors include Graeme Henkelman, Jian Han, Lei Li, Bai Li, Jie Hou, Jiaqi Wang, Wei Guo and Chuan Zhou and has published in prestigious journals such as Acta Materialia and Journal of Chemical Theory and Computation.

In The Last Decade

Zichen Song

1 paper receiving 10 citations

Peers

Zichen Song
Shi Zhang United States
J. Pletzer-Zelgert Germany
Pedro Ortiz Suárez Japan
Akhil Shajan United States
L. L. Wang China
Andre Niyongabo Rubungo United States
D. John United States
Xiang Fu United States
J. Yun Netherlands
A. Mishkin United States
Shi Zhang United States
Zichen Song
Citations per year, relative to Zichen Song Zichen Song (= 1×) peers Shi Zhang

Countries citing papers authored by Zichen Song

Since Specialization
Citations

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

Fields of papers citing papers by Zichen Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zichen Song

This figure shows the co-authorship network connecting the top 25 collaborators of Zichen Song. A scholar is included among the top collaborators of Zichen Song 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 Zichen Song. Zichen Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

3 of 3 papers shown
1.
2.
Song, Zichen, Jian Han, Graeme Henkelman, & Lei Li. (2024). Charge-Optimized Electrostatic Interaction Atom-Centered Neural Network Algorithm. Journal of Chemical Theory and Computation. 20(5). 2088–2097. 10 indexed citations
3.
Wang, Jiaqi, et al.. (2024). Automatic Feature Selection for Atom-Centered Neural Network Potentials Using a Gradient Boosting Decision Algorithm. Journal of Chemical Theory and Computation. 20(23). 10564–10573.

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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2026