Hieu‐Chi Dam

1.9k total citations
87 papers, 1.4k citations indexed

About

Hieu‐Chi Dam is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Hieu‐Chi Dam has authored 87 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Materials Chemistry, 15 papers in Electrical and Electronic Engineering and 15 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Hieu‐Chi Dam's work include Machine Learning in Materials Science (21 papers), Graphene research and applications (12 papers) and X-ray Diffraction in Crystallography (11 papers). Hieu‐Chi Dam is often cited by papers focused on Machine Learning in Materials Science (21 papers), Graphene research and applications (12 papers) and X-ray Diffraction in Crystallography (11 papers). Hieu‐Chi Dam collaborates with scholars based in Japan, Vietnam and United Kingdom. Hieu‐Chi Dam's co-authors include Berndt Sjöberg, Jens Toft, Yoshihiro Iwasa, Kosmas Prassides, Hiori Kino, Taishi Takenobu, Takayoshi Ito, Tadaoki Mitani, Nguyen Thanh Cuong and Takashi Miyake and has published in prestigious journals such as Journal of the American Chemical Society, The Journal of Chemical Physics and Physical review. B, Condensed matter.

In The Last Decade

Hieu‐Chi Dam

83 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hieu‐Chi Dam Japan 22 866 298 272 206 141 87 1.4k
V. L. Karen United States 12 1.1k 1.3× 293 1.0× 274 1.0× 351 1.7× 38 0.3× 21 1.6k
Shi‐Yuan Zhang China 24 989 1.1× 269 0.9× 144 0.5× 508 2.5× 152 1.1× 102 2.0k
Qian Gao China 21 858 1.0× 517 1.7× 90 0.3× 126 0.6× 76 0.5× 87 1.5k
Janine George Germany 20 1.3k 1.5× 533 1.8× 130 0.5× 298 1.4× 140 1.0× 54 1.9k
Pramod Kumar India 21 589 0.7× 355 1.2× 377 1.4× 545 2.6× 68 0.5× 139 1.5k
Ying Xiang China 21 625 0.7× 420 1.4× 219 0.8× 413 2.0× 165 1.2× 101 1.5k
Mustafa Kurban Türkiye 23 952 1.1× 480 1.6× 72 0.3× 200 1.0× 263 1.9× 81 1.3k
K. Iyakutti India 24 1.6k 1.9× 569 1.9× 230 0.8× 493 2.4× 66 0.5× 209 2.2k
Jitendra Kumar India 19 637 0.7× 274 0.9× 43 0.2× 268 1.3× 240 1.7× 69 1.3k
You Li China 24 942 1.1× 330 1.1× 49 0.2× 169 0.8× 302 2.1× 93 1.5k

Countries citing papers authored by Hieu‐Chi Dam

Since Specialization
Citations

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

Fields of papers citing papers by Hieu‐Chi Dam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hieu‐Chi Dam. 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 Hieu‐Chi Dam. The network helps show where Hieu‐Chi Dam may publish in the future.

Co-authorship network of co-authors of Hieu‐Chi Dam

This figure shows the co-authorship network connecting the top 25 collaborators of Hieu‐Chi Dam. A scholar is included among the top collaborators of Hieu‐Chi Dam 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 Hieu‐Chi Dam. Hieu‐Chi Dam 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.
Dinh, Duy-Tai, et al.. (2025). Categorical data clustering: 25 years beyond K-modes. Expert Systems with Applications. 272. 126608–126608. 7 indexed citations
2.
Tran, Truyen, Hiori Kino, Nozomu Ishiguro, et al.. (2025). PID3Net: a deep learning approach for single-shot coherent X-ray diffraction imaging of dynamic phenomena. npj Computational Materials. 11(1).
3.
4.
Huan, Tran Doan, Hieu‐Chi Dam, Christopher Künneth, Vu Ngoc Tuoc, & Hiori Kino. (2024). Superconductor Discovery in the Emerging Paradigm of Materials Informatics. Chemistry of Materials. 36(22). 10939–10966. 2 indexed citations
5.
Matsui, H., Naoya Amino, Tomoya Uruga, et al.. (2023). Machine learning-derived reaction statistics for 3D spectroimaging of copper sulfidation in heterogeneous rubber/brass composites. Communications Materials. 4(1). 3 indexed citations
6.
Tran, Truyen, Tran Doan Huan, Hiori Kino, et al.. (2023). Towards understanding structure–property relations in materials with interpretable deep learning. npj Computational Materials. 9(1). 23 indexed citations
7.
Kino, Hiori, Yasunobu Ando, Takashi Miyake, et al.. (2023). Evidence-based data mining method to reveal similarities between materials based on physical mechanisms. Journal of Applied Physics. 133(5). 1 indexed citations
8.
9.
Kino, Hiori, et al.. (2021). Characterization of descriptors in machine learning for data-based sputtering yield prediction. Physics of Plasmas. 28(1). 21 indexed citations
10.
Nagata, Takahiro, Toyohiro Chikyow, Hiori Kino, et al.. (2021). Evidence-based recommender system for high-entropy alloys. Nature Computational Science. 1(7). 470–478. 23 indexed citations
11.
12.
Kino, Hiori, Kohji Nakamura, Koji Hukushima, Takashi Miyake, & Hieu‐Chi Dam. (2020). Maximum Separated Distribution with High Interpretability Found Using an Exhaustive Search Method —Application to Magnetocrystalline Anisotropy of Fe/Co Films—. Journal of the Physical Society of Japan. 89(6). 64802–64802. 2 indexed citations
13.
Thakur, Ashutosh, et al.. (2020). Solvent screening for efficient chemical exfoliation of graphite. 2D Materials. 8(1). 15019–15019. 12 indexed citations
14.
Dam, Hieu‐Chi, et al.. (2018). Important Descriptors and Descriptor Groups of Curie Temperatures of Rare-earth Transition-metal Binary Alloys. Journal of the Physical Society of Japan. 87(11). 113801–113801. 23 indexed citations
15.
Tran, Truyen, et al.. (2018). Committee machine that votes for similarity between materials. IUCrJ. 5(6). 830–840. 4 indexed citations
16.
Muruganathan, Manoharan, et al.. (2016). First-principles study of hydrogen-enhanced phosphorus diffusion in silicon. Journal of Applied Physics. 119(4). 1 indexed citations
17.
Thành, Nguyễn Văn, et al.. (2016). Ligand-Driven Exchange Coupling in Graphene-Based Magnetic Materials. MATERIALS TRANSACTIONS. 57(10). 1680–1684. 1 indexed citations
18.
Tuan, Nguyen Anh, Shin-ichi Katayama, & Hieu‐Chi Dam. (2008). A systematic study of influence of ligand substitutions on the electronic structure and magnetic properties of Mn4single-molecule magnets. Physical Chemistry Chemical Physics. 11(4). 717–729. 6 indexed citations
19.
Dam, Hieu‐Chi, et al.. (2005). Band-edge photoluminescence in nanocrystalline ZnO:In films prepared by electrostatic spray deposition. Applied Surface Science. 252(8). 2770–2775. 27 indexed citations
20.
Hurlen, Tor, et al.. (1961). Heats of Activation of the Cu/Cu(aq)++ Electrode.. Acta chemica Scandinavica/Acta chemica Scandinavica. B, Organic chemistry and biochemistry/Acta chemica Scandinavica. A, Physical and inorganic chemistry/Acta chemica Scandinavica. Series B. Organic chemistry and biochemistry/Acta chemica Scandinavica. Series A, Physical and inorganic chemistry. 15. 621–629. 5 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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