| Scalable deeper graph neural networks for high-performance materials property prediction SS Omee, SY Louis, N Fu, L Wei, S Dey, R Dong, Q Li, J Hu Patterns 3 (5), 2022 | 85 | 2022 |
| Simultaneously-collected multimodal lying pose dataset: Enabling in-bed human pose monitoring S Liu, X Huang, N Fu, C Li, Z Su, S Ostadabbas IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (1), 1106-1118, 2022 | 66* | 2022 |
| Physics guided deep learning for generative design of crystal materials with symmetry constraints Y Zhao, EMD Siriwardane, Z Wu, N Fu, M Al-Fahdi, M Hu, J Hu npj Computational Materials 9 (1), 38, 2023 | 49 | 2023 |
| Invariant representation learning for infant pose estimation with small data X Huang, N Fu, S Liu, S Ostadabbas 2021 16th IEEE International Conference on Automatic Face and Gesture …, 2021 | 46 | 2021 |
| Material transformers: deep learning language models for generative materials design N Fu, L Wei, Y Song, Q Li, R Xin, SS Omee, R Dong, EMD Siriwardane, ... Machine Learning: Science and Technology 4 (1), 015001, 2023 | 38 | 2023 |
| TCSP: a template-based crystal structure prediction algorithm for materials discovery L Wei, N Fu, EMD Siriwardane, W Yang, SS Omee, R Dong, R Xin, J Hu Inorganic Chemistry 61 (22), 8431-8439, 2022 | 34 | 2022 |
| Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials L Wei, Q Li, Y Song, S Stefanov, R Dong, N Fu, EMD Siriwardane, F Chen, ... Advanced Science 11 (36), 2304305, 2024 | 20 | 2024 |
| DeepXRD, a deep learning model for predicting XRD spectrum from material composition R Dong, Y Zhao, Y Song, N Fu, SS Omee, S Dey, Q Li, L Wei, J Hu ACS Applied Materials & Interfaces 14 (35), 40102-40115, 2022 | 17 | 2022 |
| Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study SS Omee, N Fu, R Dong, M Hu, J Hu npj Computational Materials 10 (1), 144, 2024 | 13 | 2024 |
| Probabilistic generative transformer language models for generative design of molecules L Wei, N Fu, Y Song, Q Wang, J Hu Journal of Cheminformatics 15 (1), 88, 2023 | 13 | 2023 |
| Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models N Fu, J Hu, Y Feng, G Morrison, HC Loye, J Hu Advanced Science 10 (28), 2301011, 2023 | 11* | 2023 |
| Realistic material property prediction using domain adaptation based machine learning J Hu, D Liu, N Fu, R Dong Digital Discovery 3 (2), 300-312, 2024 | 10 | 2024 |
| Global mapping of structures and properties of crystal materials Q Li, R Dong, N Fu, SS Omee, L Wei, J Hu Journal of Chemical Information and Modeling 63 (12), 3814-3826, 2023 | 6 | 2023 |
| Appearance-independent pose-based posture classification in infants X Huang, S Liu, M Wan, N Fu, DL Pino, B Modayur, S Ostadabbas International Conference on Pattern Recognition, 21-36, 2022 | 6 | 2022 |
| Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction N Fu, L Wei, J Hu The Journal of Physical Chemistry Letters 15 (10), 2841-2850, 2024 | 4 | 2024 |
| Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network D Gleaves, N Fu, EMD Siriwardane, Y Zhao, J Hu Digital Discovery 2 (2), 377-391, 2023 | 4 | 2023 |
| Infant 2D Pose Estimation and Posture Detection System S Ostadabbas, X Huang, N FU, S Liu US Patent App. 18/287,518, 2024 | 2 | 2024 |
| MD-HIT: Machine learning for material property prediction with dataset redundancy control Q Li, N Fu, SS Omee, J Hu npj Computational Materials 10 (1), 245, 2024 | 1 | 2024 |
| Generative AI for Materials Discovery: Design Without Understanding J Hu, Q Li, N Fu Engineering 39, 13-17, 2024 | 1 | 2024 |
| Physical encoding improves OOD performance in deep learning materials property prediction N Fu, SS Omee, J Hu Computational Materials Science 248, 113603, 2025 | | 2025 |
Jianjun HuProfessor of Computer Science, University of South CarolinaBestätigte E-Mail-Adresse bei cse.sc.edu
