Speaker
Description
The rapid evolution of computational materials science is redefining how materials are designed, understood, and optimized. This presentation highlights the transition from traditional first-principles modelling to data-driven materials discovery frameworks. Early work, grounded in Density Functional Theory (DFT), focused on elucidating the electronic, magnetic, and structural properties of complex systems, including strongly correlated materials and low-dimensional nanostructures will be discussed. In these studies, we established a robust structure–property relationship and these were extended to functional materials for energy applications, particularly electrocatalysts for hydrogen production and oxygen reduction, where DFT-enabled insights into adsorption energetics and reaction mechanisms guided rational materials design. In response to the growing demand for high-throughput exploration of complex chemical spaces, recent research work has incorporated machine learning (ML) techniques into the materials research modelling workflows. In this emerging paradigm, DFT calculations can provide high-fidelity datasets, while ML models act as surrogate predictors to accelerate property evaluation and materials screening. This hybrid approach enables the identification of promising candidates for catalysis, topological materials, and low-dimensional systems with significantly reduced computational cost. This work reflects a broader shift in the materials research community toward integrating physics-based and data-driven methodologies. This hybrid approach accelerates the discovery of catalytic, topological, and low-dimensional materials, reflecting a broader shift toward scalable, data-driven materials design for sustainable energy applications.
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