Hai‐Chen Wang

901 total citations
40 papers, 642 citations indexed

About

Hai‐Chen Wang is a scholar working on Materials Chemistry, Inorganic Chemistry and Condensed Matter Physics. According to data from OpenAlex, Hai‐Chen Wang has authored 40 papers receiving a total of 642 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Materials Chemistry, 11 papers in Inorganic Chemistry and 10 papers in Condensed Matter Physics. Recurrent topics in Hai‐Chen Wang's work include Machine Learning in Materials Science (11 papers), Hydrogen Storage and Materials (8 papers) and Superconductivity in MgB2 and Alloys (7 papers). Hai‐Chen Wang is often cited by papers focused on Machine Learning in Materials Science (11 papers), Hydrogen Storage and Materials (8 papers) and Superconductivity in MgB2 and Alloys (7 papers). Hai‐Chen Wang collaborates with scholars based in Germany, China and Portugal. Hai‐Chen Wang's co-authors include Miguel A. L. Marques, Silvana Botti, Bi‐Yu Tang, Jonathan Schmidt, Liuting Wei, Donghai Wu, Paul Pistor, Tiago F. T. Cerqueira, Rong‐Kai Pan and Tao-Tao Shi and has published in prestigious journals such as Advanced Materials, Nature Communications and The Journal of Physical Chemistry C.

In The Last Decade

Hai‐Chen Wang

37 papers receiving 635 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hai‐Chen Wang Germany 14 493 172 114 102 78 40 642
Camilo E. Calderón United States 8 262 0.5× 102 0.6× 81 0.7× 45 0.4× 44 0.6× 8 466
Pedram Tavadze United States 10 397 0.8× 127 0.7× 104 0.9× 52 0.5× 83 1.1× 15 543
Xiansong Liu China 16 352 0.7× 135 0.8× 377 3.3× 35 0.3× 67 0.9× 50 746
Christopher K. H. Borg United States 12 612 1.2× 258 1.5× 204 1.8× 122 1.2× 79 1.0× 17 843
Marco Esters United States 16 549 1.1× 204 1.2× 107 0.9× 242 2.4× 41 0.5× 36 772
Julia H. Yang United States 12 304 0.6× 302 1.8× 74 0.6× 63 0.6× 32 0.4× 17 569
Susumu Fujii Japan 14 368 0.7× 197 1.1× 49 0.4× 38 0.4× 29 0.4× 44 491
Aniekan Magnus Ukpong South Africa 9 1.1k 2.2× 362 2.1× 138 1.2× 33 0.3× 56 0.7× 34 1.2k
Shunbo Hu China 17 502 1.0× 310 1.8× 259 2.3× 17 0.2× 107 1.4× 43 700

Countries citing papers authored by Hai‐Chen Wang

Since Specialization
Citations

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

Fields of papers citing papers by Hai‐Chen Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hai‐Chen Wang. 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 Hai‐Chen Wang. The network helps show where Hai‐Chen Wang may publish in the future.

Co-authorship network of co-authors of Hai‐Chen Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Hai‐Chen Wang. A scholar is included among the top collaborators of Hai‐Chen Wang 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 Hai‐Chen Wang. Hai‐Chen Wang 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.
Silva, Tiago H., Théo Cavignac, Tiago F. T. Cerqueira, Hai‐Chen Wang, & Miguel A. L. Marques. (2025). Machine-learning accelerated prediction of two-dimensional conventional superconductors. Materials Horizons. 12(10). 3408–3419. 3 indexed citations
2.
Cui, Wenwen, et al.. (2025). Enhanced Superconductivity in X 4 H 15 Compounds via Hole‐Doping at Ambient Pressure. Advanced Science. 12(39). e08419–e08419.
3.
Cerqueira, Tiago F. T., Antonio Sanna, Yue‐Wen Fang, et al.. (2025). The maximum Tc of conventional superconductors at ambient pressure. Nature Communications. 16(1). 8253–8253. 6 indexed citations
4.
Schmidt, Jonathan, Tiago F. T. Cerqueira, A. Romero, et al.. (2024). Improving machine-learning models in materials science through large datasets. Materials Today Physics. 48. 101560–101560. 49 indexed citations
5.
Wang, Hai‐Chen, et al.. (2024). Elpasolite-type superstructures in inverse perovskite nitrides. Progress in Solid State Chemistry. 74. 100444–100444. 1 indexed citations
6.
Wang, Hai‐Chen, et al.. (2024). Ruddlesden–Popper Oxyfluorides La2Ni1–xCuxO3F2 (0 ≤ x ≤ 1): Impact of the Ni/Cu Ratio on the Structure. Inorganic Chemistry. 63(13). 6075–6081. 4 indexed citations
7.
Wang, Hai‐Chen, et al.. (2024). Training machine learning interatomic potentials for accurate phonon properties. Machine Learning Science and Technology. 5(4). 45019–45019. 2 indexed citations
8.
Wang, Hai‐Chen, et al.. (2024). Exploring flat-band properties in two-dimensional M3QX7 compounds. Physical Chemistry Chemical Physics. 26(32). 21558–21567. 2 indexed citations
9.
Wang, Hai‐Chen, Jonathan Schmidt, Miguel A. L. Marques, Ludger Wirtz, & A. Romero. (2023). Symmetry-based computational search for novel binary and ternary 2D materials. 2D Materials. 10(3). 35007–35007. 14 indexed citations
10.
Schmidt, Jonathan, Hai‐Chen Wang, G. Schmidt, & Miguel A. L. Marques. (2023). Machine learning guided high-throughput search of non-oxide garnets. npj Computational Materials. 9(1). 6 indexed citations
11.
Schmidt, Jonathan, Hai‐Chen Wang, Tiago F. T. Cerqueira, Silvana Botti, & Miguel A. L. Marques. (2022). A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals. Scientific Data. 9(1). 64–64. 28 indexed citations
12.
Pistor, Paul, Michaela Meyns, Maxim Guc, et al.. (2020). Advanced Raman spectroscopy of Cs2AgBiBr6 double perovskites and identification of Cs3Bi2Br9 secondary phases. Scripta Materialia. 184. 24–29. 67 indexed citations
13.
Wang, Hai‐Chen, Paul Pistor, Miguel A. L. Marques, & Silvana Botti. (2019). Double perovskites as p-type conducting transparent semiconductors: a high-throughput search. Journal of Materials Chemistry A. 7(24). 14705–14711. 52 indexed citations
14.
Wu, Rui, Hai‐Chen Wang, Yan Yang, et al.. (2019). Novel elastic evolution of carbide Mo2Ga2C under pressure: Ab initio theoretical investigation. International Journal of Modern Physics B. 33(30). 1950358–1950358.
15.
Wang, Hai‐Chen, Xiaojing Yao, Yan Yang, & Bi‐Yu Tang. (2017). Synergic effects of VLi and Ti doping on hydrogen desorption on LiBH4 (010) surface: A first-principles investigation. International Journal of Hydrogen Energy. 42(29). 18442–18451. 7 indexed citations
16.
Wang, Hai‐Chen, et al.. (2017). Main reinforcement effects of precipitation phase Mg 2 Cu 3 Si, Mg 2 Si and MgCu 2 on Mg-Cu-Si alloys by ab initio investigation. Physica B Condensed Matter. 521. 339–346. 7 indexed citations
17.
Wang, Hai‐Chen, Jianing Wang, Xuefeng Shi, Yaping Wang, & Bi‐Yu Tang. (2016). Possible new metastable Mo2Ga2C and its phase transition under pressure: a density functional prediction. Journal of Materials Science. 51(18). 8452–8460. 8 indexed citations
18.
Shi, Tao-Tao, Jianing Wang, Yaping Wang, Hai‐Chen Wang, & Bi‐Yu Tang. (2016). Atomic diffusion mediated by vacancy defects in pure and transition element (TM)-doped (TM = Ti, Y, Zr or Hf) L1 2 Al 3 Sc. Materials & Design. 108. 529–537. 16 indexed citations
19.
Wang, Hai‐Chen & C. K. Yuen. (2006). Exploiting dataflow to extract Java instruction level parallelism on a tag-based multi-issue semi in-order (TMSI) processor. International Parallel and Distributed Processing Symposium. 52–52. 2 indexed citations
20.
Wang, Hai‐Chen & C. K. Yuen. (2006). Exploiting dataflow to extract Java instruction level parallelism on a tag-based multi-issue semi in-order (TMSI) processor. National University of Singapore. 1. 9 pp.–9 pp.. 1 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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