About Me
I am Rongzhi Dong, a Ph.D. in Computer Science from the University of South Carolina. My research lies at the intersection of machine learning and materials science, focusing on generative AI and data-efficient methods for scientific discovery (AI4Science).
I conducted my doctoral research in the Machine Learning and Evolution Laboratory under the supervision of Dr. Jianjun Hu. Prior to that, I received my M.S. in Mechanical Engineering from Guizhou University and my B.S. in Engineering Mechanics from China Agricultural University.
Research Focus
- Generative AI for materials discovery
- Small-data learning (in-context learning, active learning)
- Out-of-distribution generalization
- Geometric deep learning & equivariant networks
Selected Publications
Q. Li†, R.Dong†, N. Miklaucic, J. Hu, S. S. Omee, L. Wei, S. Dey, M. Hu, and J. Hu, “In context learningfoundation models for materials property prediction with small datasets”, nature npj Computational Materials, 2026. paper
R. Dong, N. Fu, E. M. Siriwardane, and J. Hu, “Generative design of inorganic compounds using deep diffusion language models”, The Journal of Physical Chemistry A, vol. 128, no. 29, pp. 5980–5989, 2024. paper
J. Hu, D. Liu, N. Fu, and R. Dong*, “Realistic material property prediction using domain adaptation based machine learning”, Digital Discovery, vol. 3, no. 2, pp. 300–312, 2024. paper
R. Dong, Y. Song, E. Siriwardane, and J. Hu, “Discovery of 2d materials using transformer network based generative design”, Advanced Intelligent Systems, p. 2 300 141, 2023. paper
R. Dong, Y. Zhao, Y. Song, N. Fu, S. S. Omee, S. Dey, Q. Li, L. Wei, and J. Hu, “Deepxrd, a deep learning model for predicting xrd spectrum from material composition”, ACS Applied Materials & Interfaces, vol. 14, no. 35, pp. 40 102–40 115, 2022. paper
Academic Service
Reviewer of Refereed Journals
- Journal of the American Chemical Society
- Advanced Science
- npj Computational Materials
- IEEE Access
- Scientific Reports
- Digital Discovery
- Langmuir
- Machine Learning
- Chemical Physics Letters
Reviewer of Refereed Conferences
- IEEE TMC
- Globecom
- VTC
- PIMRC
Work Experience
Lawrence Livermore National Laboratory (LLNL), USA
Materials Science Intern, May 2024 – Aug 2024
Developed diffusion-based generative models for carbon single-crystal interfaces, enabling atomistic-level structure generation and property modeling.
AI4Science Summer Program, South Carolina, USA
Instructor, May 2025 – Aug 2025
Delivered lectures on AI-driven scientific discovery, covering machine learning fundamentals and generative models, and mentored students on research projects.
Full Publications
See full list on my Google Scholar