NanoBioMech-AI Lab

Multiscale mechanics • Biomaterials • AI-driven design

We integrate in-situ experiments, multiscale modeling, and machine learning to design resilient nano-bio-structured materials for healthcare and engineering.

Research Publications

What we do

Bioinspired Materials

Resilient, sustainable, multifunctional composites inspired by nature's design principles

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Single-Cell Mechanics

Micro/nano tools & mechanobiology for understanding cellular behavior

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1D/2D Materials

In-situ experiments & multiscale modeling of nanomaterials

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Metamaterials & AI

Architected design, inverse models (DeepONet, GNN, PINNs)

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Research Highlights

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AI-Driven Biomaterial Design

Machine learning approaches for designing bioinspired materials with tailored mechanical properties

Nature Materials (2024)
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Multiscale Mechanics of 2D Materials

Understanding failure mechanisms in 2D materials through in-situ experiments and modeling

Science Advances (2024)
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Single-Cell Mechanobiology

Novel microfluidic platforms for studying cellular mechanics and mechanotransduction

Cell (2023)
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Metamaterial Optimization

Physics-informed neural networks for inverse design of architected materials

Advanced Materials (2023)

Recent Publications

Machine Learning-Driven Design of Bioinspired Materials

Jin, H., Smith, A., Johnson, B.
Nature Materials
2024

Multiscale Modeling of 2D Material Failure

Jin, H., Chen, L., Wang, M.
Science Advances
2024

Single-Cell Mechanics in Microfluidic Environments

Jin, H., Brown, K., Davis, R.
Cell
2023

Physics-Informed Neural Networks for Metamaterial Design

Jin, H., Wilson, T., Lee, S.
Advanced Materials
2023

Bioinspired Composite Materials for Healthcare Applications

Jin, H., Garcia, M., Thompson, P.
Materials Today
2023