Computational materials science encompasses a broad and rapidly evolving set of approaches that together enable the modeling, prediction, and interpretation of materials behavior across a wide range of length and time scales. Continuous advances in theoretical frameworks, numerical algorithms, and high-performance computing have dramatically expanded the scope and predictive power of these methods. At the same time, the integration of machine learning, high-throughput computation, and data-driven modeling with physics-based approaches is unlocking new strategies for navigating vast materials spaces and uncovering complex relationships between structure, dynamics, and functionality. Together, these developments reflect a broader shift in which computation has moved from a supportive role to a driving force in how materials are understood and discovered.
This Collection highlights recent advances in computational methods that deepen fundamental understanding and enable predictive modeling across diverse classes of materials and physical phenomena. We welcome contributions spanning: the development of new methodologies and algorithms; advances in electronic structure, density functional theory, and many-body methods; multiscale and atomistic simulations of structural, dynamical, and transport properties; high-throughput and data-driven approaches to materials discovery; and the integration of machine learning with physics-based modeling. The Collection also encompasses computational studies that deliver new mechanistic insight into materials behavior, from correlated electron systems and topological phases to interfaces, defects, and functional materials under realistic conditions, leveraging state-of-the-art approaches. This Collection aims to reflect the breadth and ambition of modern computational materials science, and to highlight its growing role in accelerating both fundamental understanding and the rational design of materials.
