Sunghwan Choi

1.9k total citations · 1 hit paper
33 papers, 1.6k citations indexed

About

Sunghwan Choi is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Sunghwan Choi has authored 33 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Materials Chemistry, 12 papers in Atomic and Molecular Physics, and Optics and 11 papers in Computational Theory and Mathematics. Recurrent topics in Sunghwan Choi's work include Advanced Chemical Physics Studies (11 papers), Machine Learning in Materials Science (9 papers) and Computational Drug Discovery Methods (7 papers). Sunghwan Choi is often cited by papers focused on Advanced Chemical Physics Studies (11 papers), Machine Learning in Materials Science (9 papers) and Computational Drug Discovery Methods (7 papers). Sunghwan Choi collaborates with scholars based in South Korea, United States and Canada. Sunghwan Choi's co-authors include Woo Youn Kim, Seonki Hong, Yun Suk Na, In Taek Song, Haeshin Lee, Yeonjoon Kim, Jaewook Kim, Hyungjun Kim, Ji Young Park and Jin Woo Kim and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and Advanced Functional Materials.

In The Last Decade

Sunghwan Choi

31 papers receiving 1.6k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Non‐Covalent Self‐Assembly and Covalent Polymerization Co‐Contribute to Polydopamine Formation

2012 1.2k citations Sunghwan Choi, Woo Youn Kim et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sunghwan Choi South Korea	12	568	483	459	302	289	33	1.6k
Xueying Huang China	20	318 0.6×	611 1.3×	718 1.6×	454 1.5×	173 0.6×	60	1.9k
Chao Pan China	22	191 0.3×	374 0.8×	627 1.4×	407 1.3×	470 1.6×	70	1.7k
Weinan Xu United States	26	293 0.5×	773 1.6×	793 1.7×	289 1.0×	394 1.4×	67	2.1k
Jörg Friedrich Germany	23	683 1.2×	517 1.1×	495 1.1×	436 1.4×	140 0.5×	69	1.7k
He Cheng China	22	239 0.4×	535 1.1×	516 1.1×	237 0.8×	569 2.0×	93	2.2k
Ying Zhou China	23	219 0.4×	828 1.7×	485 1.1×	847 2.8×	113 0.4×	98	1.9k
Manish M. Kulkarni India	20	423 0.7×	791 1.6×	482 1.1×	243 0.8×	91 0.3×	55	1.8k
Yu‐Min Yang Taiwan	24	189 0.3×	622 1.3×	424 0.9×	368 1.2×	123 0.4×	80	1.9k
Blair Brettmann United States	21	286 0.5×	282 0.6×	372 0.8×	125 0.4×	354 1.2×	51	1.3k
Simon J. Holder United Kingdom	28	372 0.7×	972 2.0×	456 1.0×	337 1.1×	445 1.5×	93	2.4k

Countries citing papers authored by Sunghwan Choi

Since Specialization

Citations

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

Fields of papers citing papers by Sunghwan Choi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sunghwan Choi

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Shaker, Bilal, Sunghwan Choi, Sunghwan Choi, et al.. (2025). Employing Automated Machine Learning (AutoML) Methods to Facilitate the In Silico ADMET Properties Prediction. Journal of Chemical Information and Modeling. 65(7). 3215–3225. 5 indexed citations

Yeom, Min Sun, et al.. (2025). Hybridization of SMILES and chemical-environment-aware tokens to improve performance of molecular structure generation. Scientific Reports. 15(1). 16892–16892. 1 indexed citations

Kim, Woo Youn, et al.. (2024). Efficient Shift-and-Invert Preconditioning for Multi-GPU Accelerated Density Functional Calculations. Journal of Chemical Theory and Computation. 20(17). 7443–7452.

Choi, Sunghwan. (2023). Prediction of transition state structures of gas-phase chemical reactions via machine learning. Nature Communications. 14(1). 1168–1168. 32 indexed citations

Kim, Hyungjun, et al.. (2022). Machine Learning Applications for Chemical Reactions. Chemistry - An Asian Journal. 17(14). e202200203–e202200203. 29 indexed citations

Choi, Sunghwan, et al.. (2021). Transfer Learning from Simulation to Experimental Data: NMR Chemical Shift Predictions. The Journal of Physical Chemistry Letters. 12(14). 3662–3668. 29 indexed citations

Kim, Hyung‐Jun, Ji Young Park, & Sunghwan Choi. (2019). Highly accurate G4(MP2) benchmark on QM9 database: Energy refinement and analysis of structures. Figshare. 2 indexed citations

Kim, Hyungjun, Ji Young Park, & Sunghwan Choi. (2019). Energy refinement and analysis of structures in the QM9 database via a highly accurate quantum chemical method. Scientific Data. 6(1). 109–109. 25 indexed citations

Choi, Sunghwan, et al.. (2018). Feasibility of Activation Energy Prediction of Gas‐Phase Reactions by Machine Learning. Chemistry - A European Journal. 24(47). 12354–12358. 46 indexed citations

10.

Choi, Sunghwan, Woo Youn Kim, Min Sun Yeom, & Hoon Ryu. (2018). On the achievement of high fidelity and scalability for large‐scale diagonalizations in grid‐based DFT simulations. International Journal of Quantum Chemistry. 118(16). 1 indexed citations

11.

Kang, Sungwoo, et al.. (2016). Update to ACE‐molecule: Projector augmented wave method on lagrange‐sinc basis set. International Journal of Quantum Chemistry. 116(8). 644–650. 8 indexed citations

12.

Ryu, Seongok, et al.. (2016). Supersampling method for efficient grid-based electronic structure calculations. The Journal of Chemical Physics. 144(9). 94101–94101. 8 indexed citations

13.

Choi, Sunghwan, et al.. (2016). Performance of heterogeneous computing with graphics processing unit and many integrated core for hartree potential calculations on a numerical grid. Journal of Computational Chemistry. 37(24). 2193–2201. 8 indexed citations

14.

Choi, Sunghwan, et al.. (2015). Computational Study of Hydrogen Chemisorption on a Multi‐Phenyl Organic Linker as a Model of Hydrogen Spillover on Metal‐Organic Frameworks^#. Bulletin of the Korean Chemical Society. 36(3). 777–783. 2 indexed citations

15.

Kim, Jaewook, et al.. (2015). Feature of Exact Exchange Kohn–Sham Orbitals with Krieger–Li–Iafrate Approximation^#. Bulletin of the Korean Chemical Society. 36(3). 998–1007. 10 indexed citations

16.

Kim, Yeonjoon, Sunghwan Choi, & Woo Youn Kim. (2014). Efficient Basin-Hopping Sampling of Reaction Intermediates through Molecular Fragmentation and Graph Theory. Journal of Chemical Theory and Computation. 10(6). 2419–2426. 43 indexed citations

17.

Jeong, Jin-Woo, et al.. (2012). Efficient Blind Estimation of Block Interleaver Parameters. The Journal of Korean Institute of Communications and Information Sciences. 37(5C). 384–392. 1 indexed citations

18.

Yoon, Dongweon, et al.. (2011). Blind reconstruction of a helical scan interleaver. 1–4. 6 indexed citations

19.

Choi, Sunghwan, et al.. (2011). High Temperature Tensile Deformation Behavior of New Heat Resistant Aluminum Alloy. MATERIALS TRANSACTIONS. 52(8). 1661–1666. 8 indexed citations

20.

Choi, Sunghwan, et al.. (2011). High Temperature Oxidation Behavior of Ni based Porous Metal. Journal of Korean Powder Metallurgy Institute. 18(2). 122–128. 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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