Jaechang Lim

1.0k total citations
21 papers, 610 citations indexed

About

Jaechang Lim is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology. According to data from OpenAlex, Jaechang Lim has authored 21 papers receiving a total of 610 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computational Theory and Mathematics, 9 papers in Materials Chemistry and 7 papers in Molecular Biology. Recurrent topics in Jaechang Lim's work include Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (7 papers) and Protein Structure and Dynamics (4 papers). Jaechang Lim is often cited by papers focused on Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (7 papers) and Protein Structure and Dynamics (4 papers). Jaechang Lim collaborates with scholars based in South Korea. Jaechang Lim's co-authors include Woo Youn Kim, Seongok Ryu, Yo Joong Choe, Jiyeon Ham, Soojung Yang, Jaewook Kim, Sungwoo Kang, Tae‐Yong Kim, Sang-Yeon Hwang and Sunghwan Choi and has published in prestigious journals such as The Journal of Chemical Physics, Scientific Reports and ACS Applied Materials & Interfaces.

In The Last Decade

Jaechang Lim

20 papers receiving 605 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jaechang Lim South Korea	9	396	354	238	58	53	21	610
Kedi Wu China	8	354 0.9×	240 0.7×	128 0.5×	22 0.4×	72 1.4×	20	494
Yongjian Li China	11	239 0.6×	286 0.8×	102 0.4×	43 0.7×	17 0.3×	43	525
Barbara Mikulak-Klucznik South Korea	8	286 0.7×	275 0.8×	325 1.4×	49 0.8×	36 0.7×	9	676
Michał D. Bajczyk Poland	7	283 0.7×	224 0.6×	293 1.2×	25 0.4×	17 0.3×	9	520
Ben Liao China	6	481 1.2×	350 1.0×	297 1.2×	43 0.7×	19 0.4×	10	678
Jiří Filipovič Czechia	12	86 0.2×	285 0.8×	68 0.3×	18 0.3×	42 0.8×	40	539
Seongok Ryu South Korea	6	295 0.7×	253 0.7×	204 0.9×	29 0.5×	26 0.5×	9	428
Agnieszka Wołos Poland	9	208 0.5×	224 0.6×	259 1.1×	18 0.3×	27 0.5×	16	601
Nosheen A. Gothard United States	9	182 0.5×	148 0.4×	232 1.0×	19 0.3×	43 0.8×	10	471

Countries citing papers authored by Jaechang Lim

Since Specialization

Citations

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

Fields of papers citing papers by Jaechang Lim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jaechang Lim

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

All Works

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20 of 20 papers shown

Hwang, Sang-Yeon, et al.. (2023). PIGNet2: a versatile deep learning-based protein–ligand interaction prediction model for binding affinity scoring and virtual screening. Digital Discovery. 3(2). 287–299. 27 indexed citations

Lim, Jaechang, et al.. (2023). Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly. Advanced Science. 10(8). e2206674–e2206674. 22 indexed citations

Lim, Jaechang, et al.. (2023). Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly (Adv. Sci. 8/2023). Advanced Science. 10(8).

Kim, Hyeongwoo, et al.. (2023). DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening. Journal of Chemical Information and Modeling. 64(7). 2432–2444. 8 indexed citations

Yang, Soojung, et al.. (2022). PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions. Chemical Science. 13(13). 3661–3673. 119 indexed citations

Lim, Jaechang, et al.. (2021). Drug-likeness scoring based on unsupervised learning. Chemical Science. 13(2). 554–565. 34 indexed citations

Lee, Jinhee, et al.. (2020). Homochiral Supramolecular Thin Film from Self-Assembly of Achiral Triarylamine Molecules by Circularly Polarized Light. Molecules. 25(2). 402–402. 13 indexed citations

Kang, Sungwoo, et al.. (2020). ACE-Molecule: An open-source real-space quantum chemistry package. The Journal of Chemical Physics. 152(12). 124110–124110. 9 indexed citations

Lim, Jaechang, et al.. (2019). Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation. Journal of Chemical Information and Modeling. 59(9). 3981–3988. 269 indexed citations

10.

Ryu, Seongok, Jaechang Lim, & Woo Youn Kim. (2018). Deeply learning molecular structure-property relationships using graph attention neural network.. arXiv (Cornell University). 5 indexed citations

11.

Kim, Jaewook, Sungwoo Kang, Jaechang Lim, & Woo Youn Kim. (2018). Study of Li Adsorption on Graphdiyne Using Hybrid DFT Calculations. ACS Applied Materials & Interfaces. 11(3). 2677–2683. 39 indexed citations

12.

Kim, Jaewook, Sungwoo Kang, Jaechang Lim, Sang-Yeon Hwang, & Woo Youn Kim. (2018). Kohn–Sham approach for fast hybrid density functional calculations in real-space numerical grid methods. Computer Physics Communications. 230. 21–26. 2 indexed citations

13.

Lim, Jaechang, Sungwoo Kang, Jaewook Kim, Woo Youn Kim, & Seol Ryu. (2017). Non-empirical atomistic dipole-interaction-model for quantum plasmon simulation of nanoparticles. Scientific Reports. 7(1). 15775–15775. 6 indexed citations

14.

Lim, Jaechang, Sunghwan Choi, Jaewook Kim, & Woo Youn Kim. (2016). Outstanding performance of configuration interaction singles and doubles using exact exchange Kohn-Sham orbitals in real-space numerical grid method. The Journal of Chemical Physics. 145(22). 224309–224309. 7 indexed citations

15.

Lim, Jaechang, et al.. (2016). Improvement of initial guess via grid‐cutting for efficient grid‐based density functional calculations. International Journal of Quantum Chemistry. 116(19). 1397–1403. 8 indexed citations

16.

Kim, Taeyoung & Jaechang Lim. (2011). Reduced feedback for capacity and fairness tradeoff in multiuser diversity. IET Communications. 5(7). 945–950. 1 indexed citations

17.

Park, Soojin & Jaechang Lim. (2005). Non-blocking supervision for uncertain discrete event systems with internal unobservable transitions. IEE Proceedings - Control Theory and Applications. 152(2). 165–170. 2 indexed citations

18.

Park, Soojin, et al.. (2004). Supervisory control of real-time discrete event systems under bounded time constraints. IEE Proceedings - Control Theory and Applications. 151(3). 347–352. 8 indexed citations

19.

Lim, Jaechang, et al.. (2004). Decentralised supervisory control of discrete event systems with mutual observability. IEE Proceedings - Control Theory and Applications. 151(3). 353–356. 1 indexed citations

20.

Park, Soojin & Jaechang Lim. (2001). Hierarchical supervisory control of discrete event systems with model uncertainty. International Journal of Systems Science. 32(6). 739–744. 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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