As we go down in size, there are a number of interesting problems that arise. All things do not simply scale down in proportion.
There's Plenty of Room at the Bottom, Richard P. Feynman, 1959
A time-honored objective in engineering sciences is the theoretical and computational understanding of multiscale problems. As the world moves towards sustainability, multiscale computational modeling can greatly accelerate scientific research and industrial productions, e.g., faster drug discovery, efficient battery design, novel energy sources exploration, and systems and materials design. My research goal is to develop computational methods to better model and simulate multiscale engineering phenomena that can bring good for mechanical, materials, and systems design with practical applications in energy, bioengineering, and manufacturing. My research briefly focuses on the following three aspects, combined with selected works.
Computational Mechanics + SciML:
- H. Zhai. Stress predictions in polycrystal plasticity using graph neural networks with subgraph training. Computational Mechanics. (2025). DOI:10.1007/s00466-025-02604-6; arXiv:2409.05169. [PDF]
H. Zhai, J. Yeo. Multiscale Mechanics of Thermal Gradient Coupled Graphene Fracture: A Molecular Dynamics Study. International Journal of Applied Mechanics (2023). DOI:10.1142/S1758825123500448; arXiv:2212.07897. [PDF]
- H. Zhai, Q. Zhou, G. Hu. Predicting micro-bubble dynamics with semi-physics-informed deep learning. AIP Advances 12, 035153; (2022). DOI:10.1063/5.0079602; arXiv:2105.07179. [PDF]
H. Zhai, T. Sands. Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning. Mathematics 10, 453; (2022). DOI:10.3390/math10030453; arXiv:2112.14707. [PDF]
H. Zhai, T. Sands. Comparison of deep learning and deterministic algorithms for control modeling. Sensors 22(17), 6362. (2022). DOI:10.3390/s22176362; arXiv:2206.08831. [PDF]
Computational Materials Science + Inverse Design:
H. Zhai, H. Hao, J. Yeo. Benchmarking Inverse Optimization Algorithms for Materials Design. APL Materials, 12, 021107 (2024). DOI:10.1063/5.0177266; arXiv:2309.02646. [PDF]
H. Zhai, J. Yeo. Controlling biofilm transport with porous metamaterials designed with Bayesian learning. Journal of the Mechanical Behavior of Biomedical Materials (2023). DOI:10.1016/j.jmbbm.2023.106127; arXiv:2305.08574. [PDF]
H. Zhai, J. Yeo. Computational design of antimicrobial active surfaces via automated Bayesian optimization. ACS Biomaterials Science & Engineering, 9, 1, 269–279 (2023). DOI:10.1021/acsbiomaterials.2c01079; arXiv:2209.00055. [PDF]
Wang, Shi, Li, Zhao, Zhai, Korani, & Yeo. Computational and data-driven modeling of solid polymer electrolytes. Digital Discovery (2023). DOI:10.1039/D3DD00078H. [PDF]
- W. Hintlian, H. Zhai, M. Berg. Engineering Design of Thermo-Magnetic Generator using Multi-objective Genetic Algorithm. (2021). [PDF]
More fun stuff can be found in my notes.