Följ
Rongzhi Dong
Rongzhi Dong
PhD Candidate, University of South Carolina
Verifierad e-postadress på email.sc.edu
Titel
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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
3542019
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
852022
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
592019
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
542020
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
452021
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
402020
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
382023
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
352020
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
342022
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
332021
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
272021
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
272021
Materials property prediction with uncertainty quantification: A benchmark study
D Varivoda, R Dong, SS Omee, J Hu
Applied Physics Reviews 10 (2), 2023
222023
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
202024
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
182021
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
172022
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
162021
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
132024
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
102024
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
102021
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Artiklar 1–20