Our lab explores fundamental questions in computational materials science through first-principles simulations, machine learning, and data-driven approaches.
Our research philosophy is centered on combining fundamental physics with advanced computational methods to accelerate the discovery of functional materials for future technologies. We believe in an interdisciplinary approach that integrates Density Functional Theory (DFT), high-throughput simulations, and artificial intelligence to develop efficient, sustainable, and scientifically impactful materials solutions. Through collaboration, innovation, and rigorous scientific inquiry, our lab aims to bridge the gap between theoretical predictions and real-world applications.
Our research focuses on the computational discovery and optimization of renewable energy materials for sustainable energy applications. Using Density Functional Theory (DFT) and electronic structure calculations, we investigate the structural, optical, and electronic properties of materials such as perovskites, photocatalysts, and semiconductor heterostructures. A major goal of the group is to understand and improve materials performance for solar energy conversion, photocatalytic water splitting, and energy harvesting technologies. Through predictive simulations, we aim to identify stable, efficient, and environmentally friendly materials for next-generation clean energy systems.
Current projects include studying defect engineering in perovskite materials, bandgap tuning for visible-light photocatalysis, and catalytic mechanisms for hydrogen generation through water splitting. The group also explores high-throughput computational screening approaches to accelerate the identification of promising energy materials. Advanced computational workflows and emerging AI-driven techniques are increasingly being integrated into these studies to improve efficiency and predictive capability.
Our group investigates the fundamental properties of quantum and superconducting materials using first-principles computational techniques. By applying Density Functional Theory (DFT) and advanced electronic structure methods, we study the electronic, magnetic, and lattice properties that govern superconductivity and other quantum phenomena. The research aims to understand the microscopic mechanisms behind material behavior and identify novel compounds with promising superconducting and functional characteristics. These studies contribute to the development of future quantum technologies, energy-efficient electronics, and advanced functional devices.
Ongoing projects include the study of pressure-dependent superconductivity, low-dimensional quantum materials, and the role of electronic correlations in emerging superconductors. The group also explores computational approaches for predicting stable quantum materials and analyzing their transport and magnetic properties. Machine learning-assisted screening methods are gradually being incorporated to accelerate the discovery of candidate superconducting materials.
Our research is expanding toward the integration of artificial intelligence, machine learning, and automated computational workflows for accelerated materials discovery. By combining physics-based simulations with data-driven approaches, we aim to rapidly identify cost-effective and high-performance materials for energy, electronic, and quantum applications. The group develops high-throughput screening pipelines that enable efficient exploration of large materials databases while reducing computational cost and discovery time. This approach helps bridge traditional computational materials science with next-generation AI-assisted scientific research.
Current efforts include machine learning-based property prediction, graph neural network models for materials screening, and workflow automation for large-scale DFT calculations. The group is also exploring the integration of AI models with first-principles simulations to improve prediction accuracy and accelerate the discovery of functional materials.
Our group develops and applies advanced computational methods to understand the fundamental electronic, structural, and magnetic behavior of complex materials systems. Using Density Functional Theory (DFT), beyond-DFT approaches, and multiscale computational techniques, we investigate how atomic-scale interactions determine material properties and functionality. The research aims to provide predictive insights into material stability, electronic transport, optical response, and phase behavior across a wide range of functional materials. These studies support the rational design of materials for energy, electronics, and quantum technologies.
Current projects include electronic band structure analysis, defect and interface engineering, charge transport studies, and computational modeling of low-dimensional materials. The group also works on improving computational workflows and integrating emerging data-driven approaches to enhance simulation efficiency and predictive accuracy.
Research activities are supported through the Principal Investigator and collaborative grants.