Xingyi Guan

491 total citations
12 papers, 331 citations indexed

About

Xingyi Guan is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Xingyi Guan has authored 12 papers receiving a total of 331 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 8 papers in Molecular Biology and 6 papers in Computational Theory and Mathematics. Recurrent topics in Xingyi Guan's work include Machine Learning in Materials Science (8 papers), Protein Structure and Dynamics (6 papers) and Computational Drug Discovery Methods (6 papers). Xingyi Guan is often cited by papers focused on Machine Learning in Materials Science (8 papers), Protein Structure and Dynamics (6 papers) and Computational Drug Discovery Methods (6 papers). Xingyi Guan collaborates with scholars based in United States, Canada and China. Xingyi Guan's co-authors include Teresa Head‐Gordon, Farnaz Heidar‐Zadeh, Jie Li, Mojtaba Haghighatlari, Itai Leven, Hongxia Hao, Akshaya Kumar Das, Meili Liu, Oufan Zhang and Martin Head‐Gordon and has published in prestigious journals such as Journal of the American Chemical Society, Nature Communications and Journal of Chemical Theory and Computation.

In The Last Decade

Xingyi Guan

12 papers receiving 328 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Xingyi Guan United States	8	224	122	91	50	40	12	331
Farhad Ramezanghorbani United States	5	252 1.1×	135 1.1×	108 1.2×	54 1.1×	60 1.5×	7	336
Marius R. Bittermann Netherlands	6	220 1.0×	93 0.8×	74 0.8×	45 0.9×	27 0.7×	13	286
Alice E. A. Allen United States	9	313 1.4×	119 1.0×	109 1.2×	83 1.7×	48 1.2×	15	466
Qiyuan Zhao United States	13	188 0.8×	142 1.2×	128 1.4×	34 0.7×	30 0.8×	27	365
Kate Huddleston United States	3	218 1.0×	164 1.3×	132 1.5×	38 0.8×	18 0.5×	4	285
Pavan Kumar Behara United States	9	206 0.9×	136 1.1×	154 1.7×	42 0.8×	32 0.8×	11	363
Mojtaba Haghighatlari United States	11	316 1.4×	163 1.3×	183 2.0×	36 0.7×	58 1.4×	15	476
Mihail Bogojeski Germany	4	190 0.8×	75 0.6×	39 0.4×	69 1.4×	27 0.7×	5	301
Stefan Heinen Canada	6	263 1.2×	152 1.2×	65 0.7×	33 0.7×	24 0.6×	12	303
Jonathan Vandermause United States	7	285 1.3×	76 0.6×	64 0.7×	38 0.8×	68 1.7×	8	349

Countries citing papers authored by Xingyi Guan

Since Specialization

Citations

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

Fields of papers citing papers by Xingyi Guan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xingyi Guan

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

Wang, Yingze, Jie Li, Xingyi Guan, et al.. (2025). A workflow to create a high-quality protein–ligand binding dataset for training, validation, and prediction tasks. Digital Discovery. 4(5). 1209–1220. 4 indexed citations

Kumar, Anup, Xingyi Guan, Eric Hermes, et al.. (2024). Analytical ab initio hessian from a deep learning potential for transition state optimization. Nature Communications. 15(1). 8865–8865. 15 indexed citations

Li, Jie, Oufan Zhang, Yingze Wang, et al.. (2024). Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design. Journal of Chemical Information and Modeling. 64(24). 9082–9097. 2 indexed citations

Heindel, Joseph P., et al.. (2023). Using Diffusion Maps to Analyze Reaction Dynamics for a Hydrogen Combustion Benchmark Dataset. Journal of Chemical Theory and Computation. 19(17). 5872–5885. 4 indexed citations

Guan, Xingyi, et al.. (2023). Using machine learning to go beyond potential energy surface benchmarking for chemical reactivity. Nature Computational Science. 3(11). 965–974. 19 indexed citations

Guan, Xingyi, Akshaya Kumar Das, Christopher J. Stein, et al.. (2022). A benchmark dataset for Hydrogen Combustion. Scientific Data. 9(1). 215–215. 18 indexed citations

Li, Bo, Xingyi Guan, Song Yang, et al.. (2022). Mechanism of the Stereoselective Catalysis of Diels–Alderase PyrE3 Involved in Pyrroindomycin Biosynthesis. Journal of the American Chemical Society. 144(11). 5099–5107. 16 indexed citations

Haghighatlari, Mojtaba, Jie Li, Xingyi Guan, et al.. (2022). NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces. Digital Discovery. 1(3). 333–343. 93 indexed citations

Witek, Jagna, Joseph P. Heindel, Xingyi Guan, et al.. (2022). M-Chem: a modular software package for molecular simulation that spans scientific domains. Molecular Physics. 121(9-10). 5 indexed citations

10.

Leven, Itai, Hongxia Hao, Xingyi Guan, et al.. (2021). Recent Advances for Improving the Accuracy, Transferability, and Efficiency of Reactive Force Fields. Journal of Chemical Theory and Computation. 17(6). 3237–3251. 57 indexed citations

11.

Guan, Xingyi, Itai Leven, Farnaz Heidar‐Zadeh, & Teresa Head‐Gordon. (2021). Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction. Journal of Chemical Information and Modeling. 61(9). 4357–4369. 13 indexed citations

12.

Haghighatlari, Mojtaba, et al.. (2020). Learning to Make Chemical Predictions: The Interplay of Feature Representation, Data, and Machine Learning Methods. Chem. 6(7). 1527–1542. 85 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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