Session

DescriptionAdvances in computational materials science are increasingly driven by the interplay between high-performance computing (HPC), algorithmic innovation, and electronic-structure theory. First-principles simulations based on density-functional theory (DFT) are now essential across physics, chemistry, and engineering, yet their scalability, accuracy, and reliability face growing challenges on modern heterogeneous and GPU-centric supercomputers. Addressing these challenges requires more than raw computational power; it demands new algorithms, rigorous error control, and hardware-aware software design. This minisymposium brings together researchers from materials science, applied mathematics, and computer science to explore emerging methods that advance atomistic materials modeling in the exascale era. Topics include mathematically rigorous error estimation in DFT, accelerated and robust self-consistent field algorithms for challenging systems, randomized and mixed-precision approaches to large-scale eigenvalue problems, and sustainable porting of electronic-structure codes to modern HPC architectures. By highlighting the co-design of algorithms, numerical methods, and hardware-aware implementations, the session offers an interdisciplinary perspective on how to achieve trustworthy, scalable, and efficient first-principles simulations on next-generation supercomputers.