AI-Driven Sustainable Materials and Interface Learning & Engineering
(AI么SMILE) Group : Designing Advance Materials for a Better Future
Our focus is on using molecular models, theory, simulations, and machine learning to the design of novel and enhanced materials for a variety of applications.
Theme 1 : Synthesis and modelling of ultra-thin membranes for gas separation
Theme 2 : AI for materials discovery (In-silico understanding of electrified interfaces)
Theme 3 : Design of MOF-based Drug Delivery Systems
Synthesis and modelling of ultra-thin membranes for gas separation
Ultra-thin membranes for gas separation represent a rapidly evolving field, with ongoing research aimed at improving the efficiency, durability, and cost-effectiveness of these membranes for practical applications in H2 production, and CO2 capture. However, as system size is reduced, there is increasing influence of end effects and associated interfacial resistance, whose relative significance varies inversely with system size, reducing the effective transport coefficient by orders of magnitude, thereby severely restricting efficiency enhancement. Understanding the source of the interfacial resistance and unraveling the underlying mechanisms are therefore critical to the success of the quest for improved efficiency through the reduction of system size to nanoscale dimensions. This understanding will also explain some of the complexities of transport in biological systems, occurring in membrane nanopores of length of the order of a nanometer, such as in aquaporins, and potentially facilitate advances in medicine.
AI for materials discovery (In-silico understanding electrified interfaces)
Our research focuses on integrating molecular simulations, data-driven modelling, and machine learning to accelerate the discovery and optimization of next-generation electrolyte materials for advanced battery technologies. We aim to establish fundamental structure-property relationships governing ion transport, interfacial stability, and electrochemical performance in energy storage systems.
Objectives
Design high-performance electrolytes for Li-ion, Na-ion, solid-state, and multivalent batteries
Understand ion solvation and transport mechanisms at the molecular scale
Predict electrochemical stability and interfacial compatibility
Accelerate materials screening using AI and high-throughput simulations
Develop safer, sustainable, and high-energy-density electrolyte systems
Design of MOF-based Drug Delivery Systems
Metal Organic Frameworks (MOFs) have emerged as a transformative platform for cancer drug delivery, addressing critical limitations of conventional chemotherapy such as poor drug solubility, rapid clearance, non specific biodistribution, and severe systemic toxicity. Unlike traditional nanocarriers, MOFs offer exceptionally high surface areas and tunable pore architectures, enabling ultrahigh drug loading capacities and programmable release profiles. Their modular composition metal clusters coordinated by organic linkers-allows precise control over particle size, surface chemistry, and stimuli-responsive behavior. In the context of cancer research, MOFs can be rationally designed to remain stable in the bloodstream while releasing therapeutic payloads specifically within the tumor microenvironment, triggered by endogenous signals such as low pH, high glutathione levels, or overexpressed enzymes. Furthermore, MOF surfaces can be functionalized with targeting ligands to enhance cancer cell selectivity, reduce off-target effects, and improve therapeutic outcomes. This structural and functional versatility positions MOFs as next-generation nanocarriers capable of delivering chemotherapeutics, nucleic acids, photosensitizers, or combination therapies directly to tumors-offering new hope for more effective and personalized cancer treatment.