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Intelligent Design of Autonomous Materials

The Group on Intelligent Design of Autonomous Materials welcomes competitive and enthusiastic applicants to conduct cutting-edge research at HKUST in Hong Kong. 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

ruizhang@ust.hk

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Our Research

Our society is currently facing unprecedented challenges in health, energy, and the environment, which have created a strong demand in new materials which are renewable, multifunctional, light-weight, and can interact with human more safely and intelligently. Soft materials are a promising candidate for this purpose. The overarching goal of the our Group is to harness soft materials, such as active matter, liquid crystals, polymers, colloids, metamaterials, and their composites 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 are promising for 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 and Mechanical Engineering.

Zeyang Mou, Yuan Li, Zhihong You and Rui Zhang#

Physical Review E 111, 065410  – published on 12 June 2025

It has been shown that an anisotropic liquid crystalline (LC) environment can be used to guide the self-propulsion dynamics of dispersed microswimmers, such as bacteria. This type of composite system is named "living nematic" (LN). In the dilute limit, bacteria are found to mainly follow the local director field. Beyond the dilute limit, however, they exhibit novel dynamical behaviors, from swirling around a spiral +1 defect pattern to forming undulating waves, and to active turbulence. Our current knowledge of how these different behaviors emerge at different population densities remains limited. Here we develop a hybrid method to simulate the dynamics of microswimmers dispersed in a nematic LC. Our method is validated by comparing to existing quasi-two-dimensional (2D) experiments, including undulated swirling around a spiral pattern and stabilized undulated jets on a periodic C-pattern. We further extend our method to three-dimensional (3D) systems by examining loop-defect dynamics. We find that the morphodynamics and destiny of a loop defect not only depend on the activity (self-propulsion velocity), effective size, and the initial distribution of the swimmers, but also rely on its winding profile. Specifically, +1/2 wedge and radial twist winding can dictate the dynamics of loop defects. By varying the characteristic reversal time, we predict that microswimmers not necessarily accumulate in splay regions. Taken together, our hybrid method provides a faithful tool to explain and guide the experiments of LNs in both 2D and 3D, sheds light on the interplay between microswimmer distribution and defect dynamics, and unravels the design principles of using LCs to control active matter.

The Intelligent Design of Autonomous Materials Group is proudly supported by the Research Grants Council of Hong Kong, Guangdong Natural Science Foundation, and ASPIRE League.

The Hong Kong University of Science and Technology

(852) 2358 5734

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