Chenru Duan

3.1k total citations · 1 hit paper
60 papers, 2.0k citations indexed

About

Chenru Duan is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Chenru Duan has authored 60 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Materials Chemistry, 28 papers in Computational Theory and Mathematics and 12 papers in Molecular Biology. Recurrent topics in Chenru Duan's work include Machine Learning in Materials Science (46 papers), Computational Drug Discovery Methods (27 papers) and Catalysis and Oxidation Reactions (12 papers). Chenru Duan is often cited by papers focused on Machine Learning in Materials Science (46 papers), Computational Drug Discovery Methods (27 papers) and Catalysis and Oxidation Reactions (12 papers). Chenru Duan collaborates with scholars based in United States, China and Singapore. Chenru Duan's co-authors include Heather J. Kulik, Aditya Nandy, Jon Paul Janet, Fang Liu, Michael G. Taylor, Tzuhsiung Yang, Adam H. Steeves, Gianmarco Terrones, Jianlan Wu and Jianshu Cao and has published in prestigious journals such as Chemical Reviews, Journal of the American Chemical Society and The Journal of Chemical Physics.

In The Last Decade

Chenru Duan

56 papers receiving 1.9k citations

Hit Papers

align trajectories

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.

Machine learning-aided generative molecular design

2024 68 citations Yuanqi Du, Chenru Duan et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chenru Duan United States	25	1.4k	567	371	293	265	60	2.0k
Jon Paul Janet Sweden	23	1.6k 1.1×	781 1.4×	528 1.4×	141 0.5×	236 0.9×	45	2.2k
Aditya Nandy United States	25	1.6k 1.2×	462 0.8×	855 2.3×	124 0.4×	306 1.2×	56	2.2k
Sandip De Switzerland	17	1.7k 1.2×	581 1.0×	131 0.4×	282 1.0×	147 0.6×	39	2.2k
Nongnuch Artrith United States	25	2.5k 1.8×	576 1.0×	127 0.3×	414 1.4×	350 1.3×	39	3.4k
Cheng Shang China	30	2.4k 1.7×	291 0.5×	351 0.9×	278 0.9×	717 2.7×	89	3.2k
Yi‐Pei Li Taiwan	20	743 0.5×	330 0.6×	331 0.9×	143 0.5×	227 0.9×	53	1.4k
Robert Pollice Canada	18	791 0.6×	335 0.6×	181 0.5×	141 0.5×	81 0.3×	39	1.7k
Philippe Schwaller Switzerland	21	2.9k 2.1×	1.3k 2.3×	182 0.5×	254 0.9×	122 0.5×	51	3.9k
Thijs Stuyver Belgium	27	780 0.6×	223 0.4×	210 0.6×	499 1.7×	146 0.6×	56	2.2k
Kenta Hongo Japan	22	1.2k 0.9×	168 0.3×	178 0.5×	256 0.9×	86 0.3×	111	2.0k

Countries citing papers authored by Chenru Duan

Since Specialization

Citations

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

Fields of papers citing papers by Chenru Duan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenru Duan

This figure shows the co-authorship network connecting the top 25 collaborators of Chenru Duan. A scholar is included among the top collaborators of Chenru Duan 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 Chenru Duan. Chenru Duan 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

Jia, Haojun, Chenru Duan, Gianmarco Terrones, Ilia Kevlishvili, & Heather J. Kulik. (2025). Computational exploration of codoped Fe and Ru single-atom catalysts for the oxygen reduction reaction. Journal of Catalysis. 448. 116163–116163. 2 indexed citations

Zhao, Qiyuan, Y. L. Han, Duo Zhang, et al.. (2025). Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models. Advanced Science. 12(34). e06240–e06240. 3 indexed citations

Nandy, Aditya, Shuwen Yue, Chenru Duan, et al.. (2023). A database of ultrastable MOFs reassembled from stable fragments with machine learning models. Matter. 6(5). 1585–1603. 56 indexed citations

Nandy, Aditya, et al.. (2023). Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

Zhang, Xue, Zhuo Wang, Mukhtar Lawan Adam, et al.. (2023). Data‐driven structural descriptor for predicting platinum‐based alloys as oxygen reduction electrocatalysts. InfoMat. 5(6). 28 indexed citations

Terrones, Gianmarco, Chenru Duan, Aditya Nandy, & Heather J. Kulik. (2023). Low-cost machine learning prediction of excited state properties of iridium-centered phosphors. Chemical Science. 14(6). 1419–1433. 25 indexed citations

Terrones, Gianmarco, et al.. (2023). Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes. The Journal of Physical Chemistry A. 128(1). 204–216. 8 indexed citations

Taylor, Michael G., et al.. (2023). Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes. The Journal of Chemical Physics. 159(2). 19 indexed citations

Ariyarathna, Isuru R., Yeongsu Cho, Chenru Duan, & Heather J. Kulik. (2023). Gas-phase and solid-state electronic structure analysis and DFT benchmarking of HfCO. Physical Chemistry Chemical Physics. 25(39). 26632–26639. 7 indexed citations

10.

Nandy, Aditya, et al.. (2023). Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models. Physical Chemistry Chemical Physics. 25(11). 8103–8116. 2 indexed citations

11.

Duan, Chenru, et al.. (2023). Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets. Journal of Cheminformatics. 15(1). 18 indexed citations

12.

Duan, Chenru, Yuanqi Du, Haojun Jia, & Heather J. Kulik. (2023). Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. Nature Computational Science. 3(12). 1045–1055. 43 indexed citations

13.

Duan, Chenru, Aditya Nandy, Gianmarco Terrones, David W. Kastner, & Heather J. Kulik. (2022). Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores. JACS Au. 3(2). 391–401. 16 indexed citations

14.

Duan, Chenru, et al.. (2022). Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis. Journal of Chemical Theory and Computation. 18(7). 4282–4292. 12 indexed citations

15.

Cho, Yeongsu, Aditya Nandy, Chenru Duan, & Heather J. Kulik. (2022). DFT-Based Multireference Diagnostics in the Solid State: Application to Metal–Organic Frameworks. Journal of Chemical Theory and Computation. 19(1). 190–197. 9 indexed citations

16.

Nandy, Aditya, et al.. (2022). MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks. Scientific Data. 9(1). 74–74. 92 indexed citations

17.

Duan, Chenru, et al.. (2022). A transferable recommender approach for selecting the best density functional approximations in chemical discovery. Nature Computational Science. 3(1). 38–47. 17 indexed citations

18.

Nandy, Aditya, et al.. (2022). Representations and strategies for transferable machine learning improve model performance in chemical discovery. The Journal of Chemical Physics. 156(7). 74101–74101. 12 indexed citations

19.

Duan, Chenru, et al.. (2019). Two-Step Detection Algorithm for Fluctuating Weak Target Based on Dynamic Programming. Journal of Engineering and Technology. 10(1). 3 indexed citations

20.

Wang, Qianlong, et al.. (2019). Dynamical scaling in the Ohmic spin-boson model studied by extended hierarchical equations of motion. The Journal of Chemical Physics. 150(8). 84114–84114. 14 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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