This continuation proposal builds directly on the successful outcomes of our completed NAISS 2025/22-644 project, in which Tetralith resources enabled three high-impact publications on altermagnetic band splitting, exotic topological states in pyrite OsS₂, and sustainable energy-related materials. We propose to advance this research by combining density functional theory (DFT) with high-throughput computational workflows and state-of-the-art machine learning (ML) techniques to accelerate the discovery of novel materials for spintronics and energy applications. The project will focus on three interconnected directions: (1) expanding high-throughput screening of altermagnetic and magnetic topological materials using ML-guided selection and surrogate models trained on our previous DFT datasets, enabling efficient exploration of larger chemical spaces while significantly reducing computational cost; (2) detailed characterization of two-dimensional (2D) materials exhibiting nonlinear Hall effect, Rashba spin-orbit splitting, shift current, and robust topological surface states, with particular emphasis on their integration into spintronic devices; and (3) investigation of energy-relevant properties, including spin-polarized transport and magnetoelectric coupling, in candidate materials for low-power electronics and sustainable energy technologies. By integrating DFT calculations (performed with VASP or equivalent codes on Tetralith) with machine-learning interatomic potentials, active learning, and property-prediction models, we will elucidate fundamental phenomena such as spin-orbit coupling, exchange interactions, and electronic band structures at unprecedented scale and accuracy. This hybrid DFT–ML approach will deliver not only new candidate materials but also open datasets and predictive models that can guide experimental synthesis and device fabrication. The proposed work will provide crucial insights for next-generation spintronic devices (e.g., altermagnet-based memory and sensors) and contribute to energy-efficient and environmentally sustainable material solutions, fully leveraging the national HPC resources at NSC.