Muhammad Umer

Computational catalysis, electrochemical energy conversion, and data-driven materials design. Employing density functional theory (DFT), ab initio molecular dynamics, and machine-learning-based force fields (MLFFs) to elucidate reaction energetics, active-site dynamics, and interfacial chemistry in electrocatalysts, membranes, and solid-electrolyte systems. Integrating multiscale modelling and ML potentials to bridge atomistic mechanisms with experimentally validated performance, enabling the rational design of metal–nonmetal based heterostructured materials for next-generation fuel cells, batteries, and sustainable energy conversion and chemical transformation technologies.