(376a) How Molecular Simulations Can Inform Business Decisions in Different Industrial Sectors: The Composelector Example

Authors: 
Fermeglia, M., University of Trieste
Laurini, E., University of Trieste
Marson, D., University of Trieste
Mio, A., University of Trieste
Pricl, S., University of Trieste
The integration of modeling and simulations 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 modeling workflows able to predict relevant material properties (aka Key Performance Indicators or KPIs) in an accurate and reliable way. With the aim of filling the gap between business processes and materials science/engineering workflows, this work presents the application – within the framework of the EU H2020 project COMPOSELECTOR – of a multi-disciplinary, multi-model approach for the accurate, reliable, efficient and cost-effective industry-driven KPIs determination for PNC materials.
Specifically, the results obtained for case studies pertaining to aerospace (AIRBUS S.A.S.), automotive (DOW Chemicals Europe SA) and tire (Goodyear Tire & rubber Company) applications and the relevant KPIs determination (i.e., mechanical, viscoelastic and interfacial properties) will be presented and
discussed. The high degree of modeling and data prediction integration into MSPs will ultimately yield valuable information about current “holes in property space”, enable what-if scenarios and provide mechanistic insights into the high performance of these fascinating materials.