In this research direction, we explore computational and data-driven methodologies for the design, optimization, and analysis of next-generation materials, devices, and energy systems. The topics covered under this track include Density Functional Theory (DFT) simulations, SCAPS-1D, SETFOS, and other numerical simulation tools for investigating the electronic, optical, structural, and transport properties of materials and devices. In addition, we also focus on Machine Learning (ML) and Artificial Intelligence (AI)-based methodologies, including Artificial Neural Networks (ANNs), Deep Learning, Graph Neural Networks (GNNs), and Physics-Informed Machine Learning (PIML), for materials discovery, property prediction, device optimization, defect analysis, and energy system modelling.