TopicM: Modelling and Simulation
Materials science and engineering is undergoing a major shift of paradigms towards more efficient digitalization and integration and reuse of data and knowledge from modelling, characterization, synthesis and production. This is manifested by the emerging of multiple disciplines falling under the overarching topics of
- Materials Informatics that include machine learning, big-data and statistical inference allow discovering new materials, and new optimized processes tailored to producing materials with specific properties and microstructures,
- Digitalization of materials, Industry 4.0 and Material 4.0 aim at creating a digital twin of the material allowing enhanced reliability and predictability of properties under operating conditions and
- Integrated computational materials engineering (ICME) ICME is an approach for solving advanced engineering problems by integrating data from characterization with modelling.
In particular, existing challenges in all these fields are related to questions on how to best to collect, structure and organize the data, how to efficiently extract knowledge from data, how to exploit this knowledge for future innovation and how to use and re-use it in decision making in business contexts and how to enable seamless exchange of data and knowledge between different steps in processing, modelling, and synthesis.
A key to the digitalization of materials and to enable leveraging the advantages and opportunities of the digital age is an interoperable digital representation of materials and processes. There are multiple approaches to achieving such an interoperability and digitalization, spanning ad-hoc solutions that focus on syntactic metadata to fully fledged ontologies based semantic interoperability as that developed recently with in the European Materials Modelling Council (EMMC).
In this symposium, we call for an open discussion and exchange of the recent technical and scientific challenges involved in developing and interoperable representation of materials and process as driver for the digital materials age. These include recent developments ontologies, materials data schemas that allow representation of workflows, processes and materials in a digitalized manner. Artificial intelligence and machine learning approaches that leverage these digitalized representations and last but not least, avenues to integrating various existing simulation and characterization tools into common open integrated simulation platforms.