Nihang Fu

706 citations

20 papers · 473 · h-index 13

Impact in

Materials Chemistry
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
Computational Theory and Mathematics top 5%
- Computational Drug Discovery Methods

Papers in

Materials Chemistry 18
- Machine Learning in Materials Science 18
- X-ray Diffraction in Crystallography 10
- Electronic and Structural Properties of Oxides 2
Computational Theory and Mathematics 8
- Computational Drug Discovery Methods 8

Co-authors: Jianjun Hu (16 shared papers)Rongzhi Dong (10 shared papers)Sadman Sadeed Omee (9 shared papers)Edirisuriya M. Dilanga Siriwardane (6 shared papers)Ming Hu (2 shared papers)Shuangjun Liu (2 shared papers)Xiaofei Huang (2 shared papers)Sarah Ostadabbas (2 shared papers)
Journals: npj Computational Materials (3 papers)Advanced Science (2 papers)Computational Materials Science (2 papers)Engineering (1 paper)The Journal of Physical Chemistry A (1 paper)
Partner nations: United States Sri Lanka China

In The Last Decade

Nihang Fu

19 papers receiving 465 citations

Peers

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-authors

The 23 scholars most cited alongside Nihang Fu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Nihang Fu Line = papers co-authored together Nihang Fu links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

#	Work
1	Scalable deeper graph neural networks for high-performance materials property prediction Patterns ·Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, Jianjun Hu	2022	79
2	Physics guided deep learning for generative design of crystal materials with symmetry constraints npj Computational Materials ·Yong Zhao, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu, Nihang Fu, Mohammed Al‐Fahdi, Ming Hu, Jianjun Hu	2023	75
3	Simultaneously-Collected Multimodal Lying Pose Dataset: Enabling In-Bed Human Pose Monitoring IEEE Transactions on Pattern Analysis and Machine Intelligence ·Shuangjun Liu, Xiaofei Huang, Nihang Fu, Cheng Li, Zhongnan Su, Sarah Ostadabbas	2022	56
4	Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study npj Computational Materials ·Sadman Sadeed Omee, Nihang Fu, Rongzhi Dong, Ming Hu, Jianjun Hu	2024	38
5	Invariant Representation Learning for Infant Pose Estimation with Small Data Xiaofei Huang, Nihang Fu, Shuangjun Liu, Sarah Ostadabbas	2021	30
6	Material transformers: deep learning language models for generative materials design Machine Learning Science and Technology ·Nihang Fu, Lai Wei, Yuqi Song, Qinyang Li, Rui Xin, Sadman Sadeed Omee, Rongzhi Dong, Edirisuriya M. Dilanga Siriwardane, Jianjun Hu	2022	27
7	TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery Inorganic Chemistry ·Lai Wei, Nihang Fu, Edirisuriya M. Dilanga Siriwardane, Wenhui Yang, Sadman Sadeed Omee, Rongzhi Dong, Rui Xin, Jianjun Hu	2022	26
8	DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition ACS Applied Materials & Interfaces ·Rongzhi Dong, Yong Zhao, Yuqi Song, Nihang Fu, Sadman Sadeed Omee, Sourin Dey, Qinyang Li, Lai Wei, Jianjun Hu	2022	26
9	Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials Advanced Science ·Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Rongzhi Dong, Nihang Fu, Edirisuriya M. Dilanga Siriwardane, Fanglin Chen, Jianjun Hu	2024	17
10	Realistic material property prediction using domain adaptation based machine learning Digital Discovery ·Jeffrey Hu, David Liu, Nihang Fu, Rongzhi Dong	2023	17
11	Probabilistic generative transformer language models for generative design of molecules Journal of Cheminformatics ·Lai Wei, Nihang Fu, Yuqi Song, Qian Wang, Jianjun Hu	2023	16
12	MD-HIT: Machine learning for material property prediction with dataset redundancy control npj Computational Materials ·Qin Li, Nihang Fu, Sadman Sadeed Omee, Jianjun Hu	2024	15
13	Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models Advanced Science ·Nihang Fu, Jeffrey Hu, Jeffrey Hu, Ying Feng, Gregory Morrison, Hans‐Conrad zur Loye, (unknown), (unknown)	2023	13
14	Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network Digital Discovery ·Daniel Gleaves, Nihang Fu, Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Jianjun Hu	2023	12
15	Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction The Journal of Physical Chemistry Letters ·Nihang Fu, Lai Wei, Jianjun Hu	2024	9
16	Global Mapping of Structures and Properties of Crystal Materials Journal of Chemical Information and Modeling ·Qinyang Li, Rongzhi Dong, Nihang Fu, Sadman Sadeed Omee, Lai Wei, Jianjun Hu	2023	7
17	Generative AI for Materials Discovery: Design Without Understanding Engineering ·Jianjun Hu, Li Qin, Nihang Fu	2024	5
18	Physical encoding improves OOD performance in deep learning materials property prediction Computational Materials Science ·Nihang Fu, Sadman Sadeed Omee, Jianjun Hu	2024	3
19	Generative Design of Inorganic Compounds Using Deep Diffusion Language Models The Journal of Physical Chemistry A ·Rongzhi Dong, Nihang Fu, Edirisuriya M. Dilanga Siriwardane, Jianjun Hu	2024	2
20	TCSP 2.0: Template based crystal structure prediction with improved oxidation state prediction and chemistry heuristics Computational Materials Science ·Lai Wei, Rongzhi Dong, Nihang Fu, Sadman Sadeed Omee, Jianjun Hu	2025	0

About Nihang Fu

Nihang Fu is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Catalysis, Mechanical Engineering and Electrical and Electronic Engineering, having authored 20 papers that have together received 473 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (18 papers), X-ray Diffraction in Crystallography (10 papers), Computational Drug Discovery Methods (8 papers), Catalysis and Oxidation Reactions (4 papers), Electronic and Structural Properties of Oxides (2 papers), Human Pose and Action Recognition (2 papers), Mineral Processing and Grinding (1 paper) and Ferroelectric and Negative Capacitance Devices (1 paper). The work is most often cited by research in Materials Chemistry (308 citations), Computational Theory and Mathematics (93 citations), Catalysis (22 citations), Computer Vision and Pattern Recognition (51 citations) and Industrial and Manufacturing Engineering (15 citations). Nihang Fu has collaborated with scholars based in United States, Sri Lanka and China. Frequent co-authors include Jianjun Hu, Rongzhi Dong, Sadman Sadeed Omee, Edirisuriya M. Dilanga Siriwardane, Ming Hu, Shuangjun Liu, Xiaofei Huang, Sarah Ostadabbas, Yong Zhao and Zhenyao Wu. Their work appears in journals such as npj Computational Materials, Advanced Science, Computational Materials Science, Engineering and The Journal of Physical Chemistry A.

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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