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Intelligent Design of Autonomous Materials

The Intelligent Design of Autonomous Materials Group welcomes competitive and enthusiastic applicants to conduct cutting-edge research at HKUST. Interested persons with theoretical or computational background in Applied Mathematics, Physics, Biophysics, Materials Science, Mechanical Engineering, or Chemical Engineering are encouraged to send enquiries to Rui's email address at


Our Research

Our society is currently facing unprecedented challenges in health, energy, and environment. And there is a strong demand in new materials which are renewable, multifunctional, light-weight and can interact with human more safely and more intelligently. Soft materials are a promising candidate for the above purpose. The overarching goal of the Computational Soft Matter Group is to harness soft materials, such as active matter, multiphase or porous media, liquid crystals, polymers, colloids, and mechanical metamaterials, to design next generation, autonomous materials and soft machines.

Specifically, our group will employ traditional and emerging computational methods, including machine learning, to propose novel soft materials with nontraditional functionalities, features and dynamics. Examples include active fluids with tailorable flow patterns, multiphase systems sensitive to specific stimuli, and origami materials with novel shape-changing behaviors in response to external fields. These new soft materials could find applications in soft robotics, wearable devices, space exploration, 4D printing, energy harvesting, smart buildings, sensing and diagnosis, and etc.


Our group strives to borrow the wisdom from biological systems and design synthetic materials and machines that are low-cost, green, biocompatible and intelligent. Our research is multidisciplinary, covering Physics, Biology, Chemistry, Materials Science, Chemical Engineering, and Mechanical Engineering.

Chung Wing Chan, Zheng Yang, Zecheng Gan# and Rui Zhang#

J. Chem. Phys. 161, 014904 – published on 2 July 2024

In living and synthetic active matter systems, the constituents can self-propel and interact with each other and with the environment through various physicochemical mechanisms. Among these mechanisms, chemotactic and auto-chemotactic effects are widely observed. However, the influence of these effects on chiral active matter remains elusive. Here, we develop a Brownian dynamics model coupled with a diffusion equation to examine the dynamics of auto-chemotactic chiral active droplets in both quasi-2D and 3D systems. By quantifying the droplet trajectory as a function of the dimensionless Péclet number and chemotactic strength, our simulations well reproduce the curling and helical trajectories of nematic droplets in a surfactant-rich solution reported by Krüger et al., Phys. Rev. Lett. (2016). The modeled curling trajectory in 2D exhibits an emergent chirality, also consistent with the experiment. We further show that the geometry of the chiral droplet trajectories can be used to infer the velocities of the droplet. Interestingly, we find that, unlike the achiral case, the velocities of chiral active droplets show dimensionality dependence. As such, our particle-based simulations provide new insights into the dynamics of auto-chemotactic chiral active droplets, reveal the effects of dimensionality, and pave the way towards their applications, such as drug delivery, sensors, and micro-reactors.

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