Bio

I study mechanics of materials using computer simulations across multiple length and time scales. I am currently working on understanding how material defects and microstructures govern macroscopic mechanical behaviors. This includes constructing plasticity theory from statistics of dislocations, homogenization theory of digital rocks, and developing data-driven methods for multiscale simulations.

I did Research Interns at Mitsubishi Electric Research Labs (working on machine learning for dynamical and energy systems) and Tokyo Electron (working on computational modeling of semiconductor manufacturing).

I am currently teaching (TA & co-lecture) for Elasticity & Inelasticity (ME340). I served as the TA for Finite Element Method (ME335A).

Education & Certifications

  • M.S., Cornell University, Mechanical Engineering (2023)
  • B.S., Shanghai University, Theoretical and Applied Mechanics (2021)

Contact

  • Academic
    hzhai@stanford.edu
    University - Student Department: Mechanical Engineering Position: Graduate

Additional Info

Links

Lab Affiliations

Work Experience

  • Research Intern, Institute of Mechanics, CAS (May 2021 - August 2021)

    Location

    Beijing, China

  • Research Scientist Intern, Tokyo Electron (May 2023 - August 2023)

    Location

    Austin, TX

  • Research Intern, Mitsubishi Electric Research Labs (June 15, 2025 - September 12, 2025)

    Data-driven modeling of dynamical systems.

    Location

    Cambridge, MA, USA

All Publications

  • Link statistics of dislocation network during strain hardening JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS Akhondzadeh, S., Zhai, H., Jian, W., Sills, R. B., Bertin, N., Cai, W. 2026; 210

    View details for DOI 10.1016/j.jmps.2026.106533

    View details for Web of Science ID 001685746700001

  • Atomistic and data-driven insights into the local slip resistances in random refractory multi-principal element alloys INTERNATIONAL JOURNAL OF PLASTICITY Jian, W., Kulathuvayal, A. S., Zhai, H., Raj, A., Yao, X., Su, Y., Xu, S., Beyerlein, I. J. 2026; 199

    View details for DOI 10.1016/j.ijplas.2026.104635

    View details for Web of Science ID 001688972400001

  • Stress predictions in polycrystal plasticity using graph neural networks with subgraph training COMPUTATIONAL MECHANICS Zhai, H. 2025

    View details for DOI 10.1007/s00466-025-02604-6

    View details for Web of Science ID 001416841800001

  • Benchmarking inverse optimization algorithms for materials design APL MATERIALS Zhai, H., Hao, H., Yeo, J. 2024; 12 (2)

    View details for DOI 10.1063/5.0177266

    View details for Web of Science ID 001157670400002

  • Computational and data-driven modelling of solid polymer electrolytes DIGITAL DISCOVERY Wang, K., Shi, H., Li, T., Zhao, L., Zhai, H., Korani, D., Yeo, J. 2023

    View details for DOI 10.1039/d3dd00078h

    View details for Web of Science ID 001091846000001

  • Multiscale Mechanics of Thermal Gradient Coupled Graphene Fracture: A Molecular Dynamics Study INTERNATIONAL JOURNAL OF APPLIED MECHANICS Zhai, H., Yeo, J. 2023; 15 (06)

    View details for DOI 10.1142/S1758825123500448

    View details for Web of Science ID 000988278400001

  • Controlling biofilm transport with porous metamaterials designed with Bayesian learning JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS Zhai, H., Yeo, J. 2023; 147 (106127)

    View details for DOI 10.1016/j.jmbbm.2023.106127

  • Computational Design of Antimicrobial Active Surfaces via Automated Bayesian Optimization ACS BIOMATERIALS SCIENCE & ENGINEERING Zhai, H., Yeo, J. 2023; 9 (1): 269-279

    Abstract

    Biofilms pose significant problems for engineers in diverse fields, such as marine science, bioenergy, and biomedicine, where effective biofilm control is a long-term goal. The adhesion and surface mechanics of biofilms play crucial roles in generating and removing biofilm. Designing customized nanosurfaces with different surface topologies can alter the adhesive properties to remove biofilms more easily and greatly improve long-term biofilm control. To rapidly design such topologies, we employ individual-based modeling and Bayesian optimization to automate the design process and generate different active surfaces for effective biofilm removal. Our framework successfully generated optimized functional nanosurfaces for improved biofilm removal through applied shear and vibration. Densely distributed short pillar topography is the optimal geometry to prevent biofilm formation. Under fluidic shearing, the optimal topography is to sparsely distribute tall, slim, pillar-like structures. When subjected to either vertical or lateral vibrations, thick trapezoidal cones are found to be optimal. Optimizing the vibrational loading indicates a small vibration magnitude with relatively low frequencies is more efficient in removing biofilm. Our results provide insights into various engineering fields that require surface-mediated biofilm control. Our framework can also be applied to more general materials design and optimization.

    View details for DOI 10.1021/acsbiomaterials.2c01079

    View details for Web of Science ID 000903614000001

    View details for PubMedID 36537745

  • Predicting micro-bubble dynamics with semi-physics-informed deep learning AIP ADVANCES Zhai, H., Zhou, Q., Hu, G. 2022; 12 (3)

    View details for DOI 10.1063/5.0079602

    View details for Web of Science ID 000781381900003

  • Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning MATHEMATICS Zhai, H., Sands, T. 2022; 10 (3)

    View details for DOI 10.3390/math10030453

    View details for Web of Science ID 000759318800001

https://orcid.org/0000-0003-1511-7597