Materials Design

Our research focuses on the accelerated design and discovery of advanced materials by integrating Density Functional Theory (DFT), machine learning (ML), analytical modeling, and experimental validation. Using DFT, we predict the thermodynamic, electronic, and mechanical properties of materials at the atomic scale, providing critical insights into their behavior and performance in extreme environments. These predictions are further refined through analytical modeling, which captures the underlying physical phenomena and provides scalable frameworks to interpret complex material behaviors. Complementing these approaches, machine learning models analyze large datasets generated from computational, analytical, and experimental studies to identify patterns and optimize material properties. This synergistic approach enables rapid screening and selection of promising material compositions, significantly reducing the time and cost of development. To ensure the reliability of our findings, we employ advanced experimental techniques such as solid-state powder metallurgy and additive manufacturing to synthesize and validate the proposed materials. Comprehensive characterization, including mechanical, and microstructural analyses, ensures alignment between theoretical predictions, analytical models, and observed properties. By bridging computational tools, analytical methods, and experimental innovation, our lab is pioneering new pathways to develop energy-efficient and high-performance materials for sustainable energy and structural applications.