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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.

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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.

Fourier-Space Monte Carlo Simulations of Two-Dimensional Nematic Liquid Crystals

Wentao Tang, Xiwen Chen and Rui Zhang#

J. Chem. Phys. 161, 194902 – published on 18 November 2024

Thermal fluctuations are ubiquitous in mesoscopic and microscopic systems. Take nematic liquid crystals (LCs) as an example, their director fluctuations can strongly scatter light and give rise to random motions and rotations of topological defects and solid inclusions. These stochastic processes contain important information about the material properties of the LC and dictate the transport of the immersed colloidal particles. However, modeling thermal fluctuations of the nematic field remains challenging. Here, we introduce a new Monte Carlo simulation method, namely the Fourier-space Monte Carlo (FSMC) method, which is based on the Oseen-Frank elastic distortion energy model. This method accurately models the thermal fluctuations of a nematic LC's director field. In contrast to the traditional Real-space MC (RSMC) method, which perturbs the director locally, the FSMC method samples different eigenmodes of the director distortions in the Fourier space, aligning with the equipartition theorem. We apply FSMC to study defect fluctuations and trajectories in a two-dimensional nematic LC confined to various geometries. Our results show that FSMC can effectively sample degenerate defect configurations and reproduce long-range elastic interactions between defects. Additionally, we conduct three-dimensional molecular dynamics simulations using a coarse-grained Gay-Berne potential, which corroborates the findings from FSMC. Taken together, we have developed a new Monte Carlo method to accurately model thermal fluctuations in nematic LCs, which can be useful for searching global free-energy minimum states in nematic, smectic, and other LC mesophases, and can also be helpful for modelling the thermal motions of defects and inclusions in LCs.

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