The integration of modelling and simulation techniques to support material selection processes (MSPs) is one of the most compelling needs in advancing material industry and manufactory, due to the necessity of effective/efficient design and production of sophisticated materials, components and systems with advanced/extreme performance on a competitive time scale. In this arena, and specifically for complex structural materials such as polymer-based nanocomposites (PNCs) there is a strong industrial demand for chemistry/physics-based models and modelling workflows able to predict relevant materials properties (aka Key Performance Indicators or KPIs) in an accurate and reliable way and prior to any experimental set-up. With the aim of filling the gap between business processes and materials science/engineering workflows, this work reports the application – within the framework of the EU H2020 project COMPOSELECTOR – of a multidisciplinary, multi-model approach for the accurate, reliable, efficient and cost-effective industry-driven KPIs determination for PNC materials. Specifically, three examples of questions and answers pertaining to aerospace and automotive applications are presented and discussed.
