Supporting the Materials 4.0 digitalisation roadmap
Physics and chemistry simulation and analytical methods are now standard tools in the development of improved materials and formulations, and the use of machine learning (ML) is increasing. Each of these classes of method has advantages and drawbacks. In this webinar, we shared the results of a project that developed a framework allowing such methods to be combined, including case studies of the integration of Alchemite™ ML with CALPHAD to understand the behaviour of different phases in an alloy, and of molecular descriptors calculated from SMILES strings to study surfactants. The work contributes to the goals of the Materials 4.0 Roadmap produced by the Henry Royce Institute, and Royce discussed the Roadmap and the Materials Challenge Accelerator Programme that supported this project. Intellegens presented and demonstrate the integrated ML / modelling solution.