The development of novel advanced materials requires a wide and complex range of trusted information on materials and process behaviour, along the entire life-cycle of a material, reaching far beyond the data sets generally available to industry currently. In particular, an approach is required that provides end users with highly flexible, adaptable modelling and characterisation tools as a source of data and knowledge in critical application fields.

Proposals should address all of the following:

• Develop integrated methodologies of multi-scale and multi-technique characterisation, combined with respective multi-scale modelling and machine learning to
  • improve the reliability and quality of data;
  • understand scaling relationships in the behaviour of advanced materials;
  • develop complex structure-property correlations in advanced materials;
  • ensure complete coverage of conditions in industrial environments.
• Integrate modelling and characterisation, in particular by
  • Developing modelling methods that provide the capabilities to virtually characterise materials and enhance the interpretation of the results of particular characterisation methods in order to guide and refine experiments;
  • Developing accurate, validated physics-based models, in areas where these capabilities are a bottleneck, by utilising a combination of characterisation and machine learning to generate material and application specific parameters and equations.
• Demonstrate the functionality of the suites for the development of certain advanced materials for the green transition.
• Validate the methodologies and provide benchmarks, i.e. clear documentation of capabilities that can serve as a standard point of reference for industrial application.