Research Area

My research focuses on atomistic simulations to investigate the fundamental properties and behavior of metals, alloys, metal matrix nanocomposites, polymers, and polymer nanocomposites at the atomic and molecular scales. By using advanced computational techniques such as density functional theory (DFT) simulations, classical molecular dynamics (MD) simulations, and reactive molecular dynamics (RMD) simulations, I explore a wide range of material properties, including mechanical strength, thermal stability, chemical reactivity, corrosion resistance, and degradation mechanisms. My work also extends to energy and environmental applications, such as water splitting for hydrogen production and CO₂ conversion into sustainable fuels.

Beyond atomistic simulations, my expertise extends to multiscale modeling approaches, integrating the discrete element method (DEM) for studying granular materials and computational fluid dynamics (CFD) for analyzing fluid-solid interactions in complex systems. Additionally, I employ data-driven approaches using regression-based artificial intelligence (AI) techniques to develop predictive models for mechanical properties, enabling the optimization of materials for various engineering applications.

The overarching goal of my research is to bridge quantum mechanics, classical simulations, and machine learning-driven predictions to advance our understanding of material behavior at multiple scales. By combining these methodologies, I aim to design and optimize high-performance materials for applications in aerospace, automotive, energy storage, and structural engineering. My work contributes to the development of next-generation materials with superior mechanical strength, thermal resistance, and chemical stability, addressing critical challenges in materials science and engineering.