Theoretical & Computational Chemistry Lab

Our research focuses on enhancing the performance and stability of not only traditional Lithium-ion and Lithium-metal batteries but also next-generation systems such as Aluminum-ion and Calcium-ion batteries. We design high-efficiency heterojunction electrode materials utilizing 2D nanomaterials like MXenes, Borophene, and Graphdiyne. Furthermore, we investigate fundamental mechanisms to extend battery life and safety, including the development of high-temperature stable electrolytes and the suppression of lithium dendrite growth on iron surfaces. We conduct in-depth mechanistic studies on electrochemical catalytic reactions, including the HER, OER/ORR, CO2 reduction, and nitrogen-to-ammonia conversion (NRR). To maximize catalytic efficiency and selectivity, we design innovative catalyst architectures such as dual-atom catalysts (DACs), transition-metal-doped hexagonal boron nitride (h-BN), and graphyne-based nanoribbons. We elucidate the molecular mechanisms of neurodegenerative diseases by analyzing the abnormal aggregation of Amyloid-β and Tau proteins through the lens of histidine tautomerism. Our research extends to biomedical engineering, where we design compounds for photodynamic therapy (PDT) and develop novel antimicrobial peptides (AMPs). By integrating Artificial Intelligence with traditional DFT, we accelerate the discovery and property prediction of new materials. Our lab employs advanced frameworks, including Transformer-based models for designing functional peptides, Attention-enhanced VAE for aptamer analysis, and Graph Attention Networks (GAT) to predict catalytic reactivity. To overcome the computational limitations of classical DFT and MD, we explore the implementation of quantum computing algorithms (e.g., VQE) in chemical simulations. We aim to pred