Nihang Fu

661 total citations
20 papers, 421 citations indexed

About

Nihang Fu is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Catalysis. According to data from OpenAlex, Nihang Fu has authored 20 papers receiving a total of 421 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Materials Chemistry, 8 papers in Computational Theory and Mathematics and 4 papers in Catalysis. Recurrent topics in Nihang Fu's work include Machine Learning in Materials Science (18 papers), X-ray Diffraction in Crystallography (10 papers) and Computational Drug Discovery Methods (8 papers). Nihang Fu is often cited by papers focused on Machine Learning in Materials Science (18 papers), X-ray Diffraction in Crystallography (10 papers) and Computational Drug Discovery Methods (8 papers). Nihang Fu collaborates with scholars based in United States, Sri Lanka and China. Nihang Fu's co-authors include Jianjun Hu, Rongzhi Dong, Sadman Sadeed Omee, Edirisuriya M. Dilanga Siriwardane, Ming Hu, Sarah Ostadabbas, Xiaofei Huang, Shuangjun Liu, Yong Zhao and Yuqi Song and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACS Applied Materials & Interfaces and Inorganic Chemistry.

In The Last Decade

Nihang Fu

19 papers receiving 410 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nihang Fu United States	12	283	91	55	48	41	20	421
Mustafa Yıldız Türkiye	11	183 0.6×	43 0.5×	74 1.3×	9 0.2×	70 1.7×	22	360
Frederick Webber United States	5	209 0.7×	46 0.5×	52 0.9×	4 0.1×	73 1.8×	8	309
Kaushik Mallik Germany	7	150 0.5×	86 0.9×	88 1.6×	9 0.2×	64 1.6×	17	370
Yuxing Wang China	13	239 0.8×	6 0.1×	24 0.4×	18 0.4×	39 1.0×	37	437
Ze Yang China	8	174 0.6×	14 0.2×	73 1.3×	5 0.1×	29 0.7×	16	316
Nasir K. Memon United States	11	359 1.3×	13 0.1×	96 1.7×	218 4.5×	131 3.2×	20	695
Qin Shi China	8	132 0.5×	6 0.1×	44 0.8×	19 0.4×	39 1.0×	25	322
Hongsuk Shim South Korea	8	106 0.4×	15 0.2×	86 1.6×	56 1.2×	41 1.0×	27	277

Countries citing papers authored by Nihang Fu

Since Specialization

Citations

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

Fields of papers citing papers by Nihang Fu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nihang Fu

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

Dong, Rongzhi, et al.. (2025). TCSP 2.0: Template based crystal structure prediction with improved oxidation state prediction and chemistry heuristics. Computational Materials Science. 261. 114317–114317.

Fu, Nihang, Sadman Sadeed Omee, & Jianjun Hu. (2024). Physical encoding improves OOD performance in deep learning materials property prediction. Computational Materials Science. 248. 113603–113603. 2 indexed citations

Li, Qin, Nihang Fu, Sadman Sadeed Omee, & Jianjun Hu. (2024). MD-HIT: Machine learning for material property prediction with dataset redundancy control. npj Computational Materials. 10(1). 11 indexed citations

Fu, Nihang, et al.. (2024). Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction. The Journal of Physical Chemistry Letters. 15(10). 2841–2850. 9 indexed citations

Song, Yuqi, Rongzhi Dong, Nihang Fu, et al.. (2024). Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials. Advanced Science. 11(36). e2304305–e2304305. 13 indexed citations

Dong, Rongzhi, Nihang Fu, Edirisuriya M. Dilanga Siriwardane, & Jianjun Hu. (2024). Generative Design of Inorganic Compounds Using Deep Diffusion Language Models. The Journal of Physical Chemistry A. 128(29). 5980–5989. 2 indexed citations

Hu, Jianjun, et al.. (2024). Generative AI for Materials Discovery: Design Without Understanding. Engineering. 39. 13–17. 4 indexed citations

Omee, Sadman Sadeed, Nihang Fu, Rongzhi Dong, Ming Hu, & Jianjun Hu. (2024). Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study. npj Computational Materials. 10(1). 31 indexed citations

Zhao, Yong, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu, et al.. (2023). Physics guided deep learning for generative design of crystal materials with symmetry constraints. npj Computational Materials. 9(1). 68 indexed citations

10.

Fu, Nihang, et al.. (2023). Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network. Digital Discovery. 2(2). 377–391. 10 indexed citations

11.

Fu, Nihang, et al.. (2023). Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models. Advanced Science. 10(28). e2301011–e2301011. 12 indexed citations

12.

Fu, Nihang, et al.. (2023). Probabilistic generative transformer language models for generative design of molecules. Journal of Cheminformatics. 15(1). 88–88. 14 indexed citations

13.

Liu, David, et al.. (2023). Realistic material property prediction using domain adaptation based machine learning. Digital Discovery. 3(2). 300–312. 16 indexed citations

14.

Dong, Rongzhi, et al.. (2023). Global Mapping of Structures and Properties of Crystal Materials. Journal of Chemical Information and Modeling. 63(12). 3814–3826. 7 indexed citations

15.

Omee, Sadman Sadeed, et al.. (2022). Scalable deeper graph neural networks for high-performance materials property prediction. Patterns. 3(5). 100491–100491. 74 indexed citations

16.

Fu, Nihang, Yuqi Song, Rui Xin, et al.. (2022). Material transformers: deep learning language models for generative materials design. Machine Learning Science and Technology. 4(1). 15001–15001. 24 indexed citations

17.

Dong, Rongzhi, Yong Zhao, Yuqi Song, et al.. (2022). DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition. ACS Applied Materials & Interfaces. 14(35). 40102–40115. 25 indexed citations

18.

Fu, Nihang, Edirisuriya M. Dilanga Siriwardane, Wenhui Yang, et al.. (2022). TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery. Inorganic Chemistry. 61(22). 8431–8439. 25 indexed citations

19.

Liu, Shuangjun, et al.. (2022). Simultaneously-Collected Multimodal Lying Pose Dataset: Enabling In-Bed Human Pose Monitoring. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(1). 1106–1118. 48 indexed citations

20.

Huang, Xiaofei, Nihang Fu, Shuangjun Liu, & Sarah Ostadabbas. (2021). Invariant Representation Learning for Infant Pose Estimation with Small Data. 1–8. 26 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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