Fangyou Yan

1.3k total citations
71 papers, 1.1k citations indexed

About

Fangyou Yan is a scholar working on Computational Theory and Mathematics, Organic Chemistry and Catalysis. According to data from OpenAlex, Fangyou Yan has authored 71 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Computational Theory and Mathematics, 24 papers in Organic Chemistry and 20 papers in Catalysis. Recurrent topics in Fangyou Yan's work include Computational Drug Discovery Methods (33 papers), Ionic liquids properties and applications (20 papers) and Chemistry and Chemical Engineering (13 papers). Fangyou Yan is often cited by papers focused on Computational Drug Discovery Methods (33 papers), Ionic liquids properties and applications (20 papers) and Chemistry and Chemical Engineering (13 papers). Fangyou Yan collaborates with scholars based in China. Fangyou Yan's co-authors include Qingzhu Jia, Shuqian Xia, Peisheng Ma, Qiang Wang, Qiang Wang, Qiaoyan Shang, Jinli Zhang, Wei Li, Hao Wu and Yin‐Ning Zhou and has published in prestigious journals such as The Science of The Total Environment, Journal of Hazardous Materials and Macromolecules.

In The Last Decade

Fangyou Yan

68 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fangyou Yan China 22 378 322 246 221 200 71 1.1k
Qingzhu Jia China 22 222 0.6× 299 0.9× 290 1.2× 278 1.3× 270 1.4× 82 1.0k
Ritu Jain United States 10 337 0.9× 203 0.6× 161 0.7× 270 1.2× 195 1.0× 22 1.1k
Yongsheng Zhao China 20 823 2.2× 144 0.4× 448 1.8× 72 0.3× 184 0.9× 41 1.2k
Anita Sosnowska Poland 15 276 0.7× 137 0.4× 76 0.3× 55 0.2× 81 0.4× 36 566
Kevin G. Joback United States 4 231 0.6× 186 0.6× 824 3.3× 522 2.4× 333 1.7× 5 1.6k
Shahin Ahmadi Iran 19 67 0.2× 494 1.5× 70 0.3× 280 1.3× 201 1.0× 75 1.0k
Nabil Abdel Jabbar United Arab Emirates 19 336 0.9× 27 0.1× 277 1.1× 94 0.4× 161 0.8× 66 1.1k
Y. A. Liu United States 15 237 0.6× 68 0.2× 391 1.6× 166 0.8× 258 1.3× 34 1.2k
Guillaume Fayet France 21 53 0.1× 315 1.0× 140 0.6× 494 2.2× 551 2.8× 56 1.1k

Countries citing papers authored by Fangyou Yan

Since Specialization
Citations

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

Fields of papers citing papers by Fangyou Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fangyou Yan

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

All Works

20 of 20 papers shown
1.
Xiong, Jialiang, et al.. (2025). Boiling point enhanced vapor pressure prediction by Antoine-based quantitative structure–property relationship. Chemical Engineering Science. 306. 121269–121269. 3 indexed citations
2.
Li, Jin‐Jin, et al.. (2025). Accelerating Polyester Intelligence: Machine-Learning-Assisted Prediction of Glass Transition Temperature and Virtual Molecules Screening. ACS Applied Materials & Interfaces. 17(39). 55347–55359.
3.
Zhu, Jiayi, et al.. (2025). Data-Driven Prediction of Flory–Huggins Parameter for Quantifying Polymer–Solvent Interaction. Industrial & Engineering Chemistry Research. 64(15). 7862–7870.
4.
Yan, Fangyou, et al.. (2025). Advances in calculation of kinetic parameters in free-radical polymerization by data-driven methods. Current Opinion in Chemical Engineering. 48. 101141–101141. 1 indexed citations
5.
Xiong, Jialiang, Xiao Liu, Qiang Wang, et al.. (2024). Accurate forecasting of bioconcentration factor by incorporating quantum chemical method in the QSAR model. Journal of Water Process Engineering. 68. 106482–106482. 1 indexed citations
6.
Yan, Fangyou, Dongdong Cao, Jialiang Xiong, et al.. (2023). Atomic connectivity group contribution (ACGC) method for critical properties prediction. Chemical Engineering Science. 280. 118990–118990. 5 indexed citations
7.
Cao, Dongdong, et al.. (2023). Atomic connectivity group contribution method for predicting the boiling and melting points of organic compounds. Chemical Engineering Science. 282. 119357–119357. 4 indexed citations
8.
Wang, Xinxin, Yi Rong, Libin Yang, et al.. (2023). Pyrazine‐Functionalized Donor–Acceptor Covalent Organic Frameworks for Enhanced Photocatalytic H2 Evolution with High Proton Transport. Small. 19(23). e2207421–e2207421. 34 indexed citations
9.
Jia, Qingzhu, et al.. (2023). QSPR models for complexation performance of α-cyclodextrin and β-cyclodextrin complexes by norm indices. Chemical Engineering Science. 284. 119484–119484. 5 indexed citations
10.
Yan, Fangyou, et al.. (2023). Boosted nitrilation of dimethyl adipate with NH3 to adiponitrile over bimetallic oxide: Synergetic effect between Nb and W. Chemical Engineering Science. 281. 119121–119121. 3 indexed citations
11.
Liu, Xiao, et al.. (2023). Leave-one-ion-out cross-validation for assisting in developing robust QSPR models of ionic liquids. Journal of Molecular Liquids. 388. 122711–122711. 11 indexed citations
12.
13.
Jia, Qingzhu, et al.. (2021). A QSTR model for toxicity prediction of pesticides towards Daphnia magna. Chemosphere. 291(Pt 2). 132980–132980. 11 indexed citations
14.
Yan, Fangyou, et al.. (2020). Norm index in QSTR work for predicting toxicity of ionic liquids on Vibrio fischeri. Ecotoxicology and Environmental Safety. 205. 111187–111187. 8 indexed citations
15.
Jia, Qingzhu, et al.. (2020). Norm index-based QSPR model for describing the n-octanol/water partition coefficients of organics. Environmental Science and Pollution Research. 27(13). 15454–15462. 5 indexed citations
16.
Yan, Fangyou, et al.. (2020). Norm index-based QSAR models for acute toxicity of organic compounds toward zebrafish embryo. Ecotoxicology and Environmental Safety. 203. 110946–110946. 9 indexed citations
17.
Yan, Fangyou, Tingting Liu, Qingzhu Jia, & Qiang Wang. (2019). Multiple toxicity endpoint–structure relationships for substituted phenols and anilines. The Science of The Total Environment. 663. 560–567. 24 indexed citations
18.
Yan, Fangyou, et al.. (2019). Norm index-based QSTR model to predict the eco-toxicity of ionic liquids towards Leukemia rat cell line. Chemosphere. 234. 116–122. 24 indexed citations
19.
Jia, Qingzhu, et al.. (2019). Norm Index–Based QSAR Model for Acute Toxicity of Pesticides Toward Rainbow Trout. Environmental Toxicology and Chemistry. 39(2). 352–358. 22 indexed citations
20.
Yan, Fangyou, et al.. (2018). QSAR models for describing the toxicological effects of ILs against Candida albicans based on norm indexes. Chemosphere. 201. 417–424. 20 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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