We leverage computational physics, machine learning, and data-driven modeling to accelerate the discovery and design of advanced materials. Our research integrates DFT simulations, AI, and high-throughput screening to solve challenges in energy, electronics, and functional materials.
The research group of Professor Alamgir Kabir focuses on the computational design and understanding of quantum and functional materials using first-principles methods based on Density Functional Theory (DFT). Our work explores the electronic, magnetic, optical, and structural properties of advanced materials for applications in energy, electronics, and next-generation technologies.
We are expanding our research toward AI-driven and high-throughput materials screening by integrating machine learning, data science, and automated computational workflows with traditional physics-based simulations. Our mission is to accelerate materials discovery while fostering innovation, collaboration, and rigorous scientific research in computational materials science.
We work across multiple interconnected domains in computational materials science and condensed matter physics.
Computational investigations of perovskites, photocatalysts, and semiconductor systems for solar energy conversion and sustainable technologies using DFT-based simulations.
Learn more →First-principles exploration of structural, electronic, and magnetic properties of quantum and superconducting materials, aiming to understand fundamental quantum phenomena.
Learn more →Machine learning and high-throughput computational workflows for rapid and cost-effective materials discovery, combining artificial intelligence with physics-based simulations.
Learn more →Selected recent work from the lab.
We are always looking for motivated Master's students and undergraduate researchers passionate about computational materials science, DFT, and AI-driven discovery.