Our research group, MatInforAI, operates at the intersection of materials science, computational modeling, and artificial intelligence, with a strong focus on the discovery and design of next-generation functional materials. We investigate advanced electrode materials for Li-, Na-, and Mg-ion batteries, spintronic materials for magnetic memory and spin-based electronic devices, semiconductor materials for optoelectronic and nanoelectronic applications, and mechanically robust materials designed to withstand extreme conditions such as high pressure, temperature, and mechanical stress.

Our research is grounded in first-principles methodologies, particularly Density Functional Theory (DFT), using computational platforms such as Quantum ESPRESSO and Pymatgen. Through these tools, we analyze the structural, electronic, magnetic, mechanical, and electrochemical properties of graphene-based systems, MXenes, and other emerging low-dimensional and functional materials.

Beyond simulation-driven investigations, we integrate machine learning and materials informatics strategies to accelerate materials discovery and predictive modeling. By combining data-driven techniques—including regression models, deep neural networks, and graph neural networks—with computational materials science, our group seeks to uncover fundamental structure–property relationships. Ultimately, we aim to develop sustainable, high-performance materials that address critical technological challenges in energy, electronics, and advanced engineering systems.