| Limited data rolling bearing fault diagnosis with few-shot learning A Zhang, S Li, Y Cui, W Yang, R Dong, J Hu Ieee Access 7, 110895-110904, 2019 | 287 | 2019 |
| 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 | 60 | 2022 |
| Computational screening of new perovskite materials using transfer learning and deep learning X Li, Y Dan, R Dong, Z Cao, C Niu, Y Song, S Li, J Hu Applied Sciences 9 (24), 5510, 2019 | 50 | 2019 |
| Machine learning-based prediction of crystal systems and space groups from inorganic materials compositions Y Zhao, Y Cui, Z Xiong, J Jin, Z Liu, R Dong, J Hu ACS omega 5 (7), 3596-3606, 2020 | 45 | 2020 |
| Mlatticeabc: generic lattice constant prediction of crystal materials using machine learning Y Li, W Yang, R Dong, J Hu ACS omega 6 (17), 11585-11594, 2021 | 35 | 2021 |
| Critical temperature prediction of superconductors based on atomic vectors and deep learning S Li, Y Dan, X Li, T Hu, R Dong, Z Cao, J Hu Symmetry 12 (2), 262, 2020 | 33 | 2020 |
| Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization R Dong, Y Dan, X Li, J Hu Computational Materials Science 188, 110166, 2021 | 29 | 2021 |
| Computational prediction of critical temperatures of superconductors based on convolutional gradient boosting decision trees Y Dan, R Dong, Z Cao, X Li, C Niu, S Li, J Hu IEEE Access 8, 57868-57878, 2020 | 28 | 2020 |
| 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 | 23 | 2022 |
| Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors Y Li, R Dong, W Yang, J Hu Computational Materials Science 198, 110686, 2021 | 19 | 2021 |
| Contact map based crystal structure prediction using global optimization J Hu, W Yang, R Dong, Y Li, X Li, S Li, EMD Siriwardane CrystEngComm 23 (8), 1765-1776, 2021 | 17 | 2021 |
| Chaos analysis and stability control of the MEMS resonator via the type-2 sequential FNN L Zhao, S Luo, G Yang, R Dong Microsystem Technologies 27, 173-182, 2021 | 15 | 2021 |
| Materials property prediction with uncertainty quantification: A benchmark study D Varivoda, R Dong, SS Omee, J Hu Applied Physics Reviews 10 (2), 2023 | 14 | 2023 |
| 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 | 12 | 2023 |
| Crystal structure prediction of materials with high symmetry using differential evolution W Yang, EMD Siriwardane, R Dong, Y Li, J Hu Journal of Physics: Condensed Matter 33 (45), 455902, 2021 | 12 | 2021 |
| Alphacrystal: Contact map based crystal structure prediction using deep learning J Hu, Y Zhao, Q Li, Y Song, R Dong, W Yang, E Siriwardane arXiv preprint arXiv:2102.01620, 2021 | 9 | 2021 |
| 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 | 8 | 2022 |
| Deep learning-based prediction of contact maps and crystal structures of inorganic materials J Hu, Y Zhao, Q Li, Y Song, R Dong, W Yang, EMD Siriwardane ACS omega 8 (29), 26170-26179, 2023 | 5 | 2023 |
| 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 | 3 | 2023 |
| Materials transformers language models for generative materials design: a benchmark study N Fu, L Wei, Y Song, Q Li, R Xin, SS Omee, R Dong, EMD Siriwardane, ... arXiv preprint arXiv:2206.13578, 2022 | 2 | 2022 |
Jianjun HuProfessor of Computer Science, University of South CarolinaVerified email at cse.sc.edu
