Applications of High-Performance Computing (HPC) in Materials Simulation

High-Performance Computing (HPC) has become a fundamental pillar of modern computational materials science, enabling the simulation and analysis of increasingly complex material systems with unprecedented accuracy and efficiency. The rapid advancement of HPC architectures, including petascale and exascale computing platforms, has significantly expanded the capabilities of materials modeling and simulation. This review provides a comprehensive overview of the role of HPC in materials research, covering its applications in first-principles calculations based on Density Functional Theory (DFT), molecular dynamics (MD) simulations, high-throughput materials discovery, and the investigation of nanomaterials and two-dimensional materials. The integration of HPC with advanced computational techniques has facilitated the exploration of electronic, magnetic, optical, mechanical, and thermodynamic properties of materials across multiple length and time scales. In addition, the review discusses the growing synergy between HPC and Artificial Intelligence (AI), highlighting the emergence of Materials Informatics as a powerful paradigm for data-driven materials design and accelerated materials discovery. Major materials simulation software packages optimized for HPC environments, including VASP, Quantum ESPRESSO, CASTEP, ABINIT, SIESTA, LAMMPS, GROMACS, CP2K, and nanoDCAL, are also examined. Furthermore, current challenges related to computational scalability, energy consumption, and infrastructure costs are analyzed, together with future perspectives involving exascale computing, next-generation GPU architectures, machine learning, and quantum computing. Overall, HPC continues to play a pivotal role in advancing materials research and is expected to remain an indispensable tool for the discovery, design, and optimization of advanced materials for future technological applications.