Roadmap on novel computational approaches for bridging length and time scales: addressing challenges in modeling processes, characterization, and performance of metals and alloys

Abstract The response of metals and alloys depends on a wide range of factors ranging from composition and point defects, to larger defects such as dislocations and/or precipitates, and aspects such as the grain size, texture or other microstructural factors. Furthermore, how a material is processed and then subsequently loaded can also strongly influence material behavior. Combined, these aspects span length and time scales that vary by several orders of magnitude, providing a grand challenge to any single modeling approach. Consequently, over the past decades, multiscale modeling (MM) has emerged as a foundational tool for investigating and predicting the performance. While much development has been done, there are still open challenges in how methods bridge across different time and/or length scales, how we inform, validate, and understand complex material behavior, and more recently how emerging machine learning and artificial intelligence tools can enhance our MM approaches. Addressing these challenges and the outlook for MM on the horizon is the focus of this roadmap article. The contributions within are divided broadly into three categories: bridging the length scale gap between models, targeting timescale gaps alongside length scale gaps, and bridging the gap between experiment and simulation.