Abigail Hunter, Vinamra Agrawal, Irene Beyerlein, Somnath Ghosh, Caglar Oskay, Boyang Gu, Youping Chen, Stephen Caleb Foster, Justin Wilkerson, Xingsheng Sun, Timothy J Rupert, Ching Chen, Avinash M. Dongare, Daniel N. Blaschke, McLean P. Echlin, Tresa M Pollock, Balduin Katzer, Katrin Schulz, Zachary Croft, Marcel Chlupsa, Ashwin J. Shahani, K. Thornton, David T. Fullwood, Eric R. Homer, Emily V. White
Abstract Material response of metals and alloys depends on a wide range of factors ranging from the material’s composition and point defects, to larger defects such as dislocations and/or precipitates, and even aspects such as the grain size, texture or other microstructural factors. Furthermore, how a material is processed and then subsequently loaded also has an effect. Combined, these aspects span length and time scales that vary by several orders of magnitude, providing a grand challenge to any single modelling approach. Consequently, over the past decades, multiscale modelling has emerged as a foundational tool for investigating and predicting the performance of metals and alloys. 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 behaviour, and more recently how emerging machine learning and artificial intelligence tools can enhance our multiscale modelling approaches. Addressing these challenges and the outlook for multiscale modelling 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.