| Graph convolutional neural networks with global attention for improved materials property prediction SY Louis, Y Zhao, A Nasiri, X Wang, Y Song, F Liu, J Hu Physical Chemistry Chemical Physics 22 (32), 18141-18148, 2020 | 152 | 2020 |
| 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 |
| MaterialsAtlas. org: a materials informatics web app platform for materials discovery and survey of state-of-the-art J Hu, S Stefanov, Y Song, SS Omee, SY Louis, EMD Siriwardane, Y Zhao, ... npj Computational Materials 8 (1), 65, 2022 | 30 | 2022 |
| Predicting elastic properties of materials from electronic charge density using 3D deep convolutional neural networks Y Zhao, K Yuan, Y Liu, SY Louis, M Hu, J Hu The Journal of Physical Chemistry C 124 (31), 17262-17273, 2020 | 26 | 2020 |
| Online Damage Monitoring of SiCf-SiCm Composite Materials Using Acoustic Emission and Deep Learning A Nasiri, J Bao, D Mccleeary, SYM Louis, X Huang, J Hu IEEE Access 7, 140534-140541, 2019 | 25 | 2019 |
| Deep learning pan‐specific model for interpretable MHC‐I peptide binding prediction with improved attention mechanism J Jin, Z Liu, A Nasiri, Y Cui, SY Louis, A Zhang, Y Zhao, J Hu Proteins: Structure, Function, and Bioinformatics 89 (7), 866-883, 2021 | 23 | 2021 |
| Machine learning based prediction of noncentrosymmetric crystal materials Y Song, J Lindsay, Y Zhao, A Nasiri, SY Louis, J Ling, M Hu, J Hu Computational Materials Science 183, 109792, 2020 | 21 | 2020 |
| ACCURATE PREDICTION OF VOLTAGE OF BATTERY ELECTRODE MATERIALS USING ATTENTION BASED GRAPH NEURAL NETWORKS SY Louis, E Siriwardane, R Joshi, N Kumar, J Hu | 19 | 2021 |
| Active-learning-based generative design for the discovery of wide-band-gap materials R Xin, EMD Siriwardane, Y Song, Y Zhao, SY Louis, A Nasiri, J Hu The Journal of Physical Chemistry C 125 (29), 16118-16128, 2021 | 19 | 2021 |
| Node-select: a graph neural network based on a selective propagation technique SY Louis, A Nasiri, FJ Rolland, C Mitro, J Hu Neurocomputing 494, 396-408, 2022 | 9 | 2022 |
| Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission SY Louis, A Nasiri, J Bao, Y Cui, Y Zhao, J Jin, X Huang, J Hu Applied Sciences 10 (8), 2680, 2020 | 8 | 2020 |
| Predicting Phonon Vibrational Frequencies Using Deep Graph Neural networks N Nguyen, SY Louis, L Wei, K Choudhary, M Hu, J Hu | 6* | |
| Extending the Convolution in Graph Neural Networks to Solve Materials Science and Node Classification Problems SYM Louis University of South Carolina, 2023 | | 2023 |
| Data-enabled Fusion Technology (DeFT): Machine Learning Tools in the Ousai Platform C Michoski, D Hatch, T Oliver, D Kuang, SY Louis, S Luo, M Vitse, ... APS Division of Plasma Physics Meeting Abstracts 2021, TM10. 007, 2021 | | 2021 |
| Randomization Analysis Driven Software SY Louis University of South Carolina, 2019 | | 2019 |
Jianjun HuProfessor of Computer Science, University of South Carolina在 cse.sc.edu 的電子郵件地址已通過驗證
