Minhi Han

400 total citations
12 papers, 258 citations indexed

About

Minhi Han is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Electrical and Electronic Engineering. According to data from OpenAlex, Minhi Han has authored 12 papers receiving a total of 258 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Materials Chemistry, 4 papers in Computational Theory and Mathematics and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Minhi Han's work include Machine Learning in Materials Science (4 papers), Computational Drug Discovery Methods (4 papers) and Conducting polymers and applications (2 papers). Minhi Han is often cited by papers focused on Machine Learning in Materials Science (4 papers), Computational Drug Discovery Methods (4 papers) and Conducting polymers and applications (2 papers). Minhi Han collaborates with scholars based in South Korea and Japan. Minhi Han's co-authors include Sungnam Park, Joonyoung F. Joung, Minseok Jeong, Dong Hoon Choi, Jinhyo Hwang, Chang Woo Koh, Byung Ihn Choi, Youngseo Kim, Joon Koo Han and Seung Hoon Kim and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Physical Chemistry B and Chemical Engineering Journal.

In The Last Decade

Minhi Han

12 papers receiving 240 citations

Peers

Minhi Han

EEE

CTM

PTC

Minseok Jeong South Korea

Edward J. Beard United Kingdom

Luis Itza Vazquez-Salazar Switzerland

Kai Töpfer Switzerland

Andrea Cadeddu United States

Ian M. Pendleton United States

Farhad Ramezanghorbani United States

Georg Huhs Spain

Sayan Banerjee United States

Cihat Güleryüz Türkiye

Minseok Jeong South Korea

Minhi Han

156 ×1.3

73 ×1.1

EEE

66 ×1.0

CTM

51 ×1.3

PTC

56 ×1.5

Citations per year, relative to Minhi Han Minhi Han (= 1×) peers Minseok Jeong

Countries citing papers authored by Minhi Han

Since Specialization

Citations

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

Fields of papers citing papers by Minhi Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Minhi Han

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

All Works

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12 of 12 papers shown

Han, Minhi, Tetsuya Yokoo, Jin-Yong Park, Kenichi Oyaizu, & Sungnam Park. (2025). Deep learning prediction of ionic conductivity in polymer electrolytes using hierarchical polymer graphs. Chemical Engineering Journal. 521. 166829–166829. 2 indexed citations

Kim, Seokwoo, et al.. (2025). Machine Learning Prediction of Optical Properties of Coumarin Derivatives Using Gaussian-Weighted Graph Convolution and Subgraph Modular Input. Journal of Chemical Information and Modeling. 65(10). 4889–4897. 1 indexed citations

Han, Minhi, Joonyoung F. Joung, Minseok Jeong, Dong Hoon Choi, & Sungnam Park. (2024). Generative Deep Learning-Based Efficient Design of Organic Molecules with Tailored Properties. ACS Central Science. 11(2). 219–227. 7 indexed citations

Park, Jin-Yong, et al.. (2024). Hierarchical Graph Attention Network with Positive and Negative Attentions for Improved Interpretability: ISA-PN. Journal of Chemical Information and Modeling. 65(3). 1115–1127. 4 indexed citations

Joung, Joonyoung F., et al.. (2022). Locoregional Recurrence Prediction Using a Deep Neural Network of Radiological and Radiotherapy Images. Journal of Personalized Medicine. 12(2). 143–143. 5 indexed citations

Jeong, Minseok, Joonyoung F. Joung, Jinhyo Hwang, et al.. (2022). Deep learning for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs. npj Computational Materials. 8(1). 38 indexed citations

Joung, Joonyoung F., Minhi Han, Minseok Jeong, & Sungnam Park. (2022). Beyond Woodward–Fieser Rules: Design Principles of Property-Oriented Chromophores Based on Explainable Deep Learning Optical Spectroscopy. Journal of Chemical Information and Modeling. 62(12). 2933–2942. 12 indexed citations

Joung, Joonyoung F., Minhi Han, Jinhyo Hwang, et al.. (2021). Deep Learning Optical Spectroscopy Based on Experimental Database: Potential Applications to Molecular Design. SHILAP Revista de lepidopterología. 1(4). 427–438. 103 indexed citations

Kim, Youngseo, Minhi Han, Chiho Lee, & Sungnam Park. (2021). Singlet Fission Dynamics of Colloidal Nanoparticles of a Perylenediimide Derivative in Solutions. The Journal of Physical Chemistry B. 125(29). 7967–7974. 7 indexed citations

10.

Joung, Joonyoung F., Minhi Han, Minseok Jeong, & Sungnam Park. (2020). Experimental database of optical properties of organic compounds. Scientific Data. 7(1). 295–295. 66 indexed citations

11.

Han, Joon Koo, et al.. (1996). Tuberculous colitis. Diseases of the Colon & Rectum. 39(11). 1204–1209. 9 indexed citations

12.

Koo, Kyung‐Hoi, et al.. (1986). Angiographic diagnosis and treatment of gastrointestinal bleeding. Journal of the Korean Radiological Society. 22(1). 12–12. 4 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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