Multiscale modeling of materials promotes the development of predictive materials research tools to understand the structure and properties of materials at all scales. The field strives to use these predictive tools to design and process materials with novel properties. Multiscale modeling of materials transcends the boundaries between materials science, mechanics, and physics and chemistry of materials and is creating opportunities for making materials predictions with unprecedented levels of rigor and accuracy.
Computational modeling of materials behavior through multiscale materials modeling (MMM) approaches is becoming essential for scientific investigations, complementing traditional theoretical and experimental methods. As continuum approaches falter at transitional scales and atomistic methods face limitations, new theoretical frameworks and modeling techniques are developed to bridge these gaps. Industrial advancements in high-tech fields depend on the ability to engineer materials for enhanced performance efficiently and cost-effectively. This requires rapid development of processing techniques and a deeper understanding of the relationships among material chemistry, processing, structure, performance, and durability. Such complexities often necessitate multi-scale and multi-stage modeling or simulation. In high-risk sectors like aerospace and nuclear industries, understanding aging and environmental effects on failure mechanisms demands innovative MMM approaches for new material systems. Validation experiments are vital to ensure models accurately predict behaviors across scales, minimizing reliance on empirical fitting. The rising interest in nanotechnology further challenges researchers to design devices on the nanoscale for next-generation applications. As computational power and MMM methodologies advance, they increasingly enable the discovery of new materials and functionalities, marking a shift from traditional trial-an
