When designing and optimizing materials and manufacturing processes, numerous combinations must be tested. Utilising computational tools for automated screening, such as high-throughput screening (HTS), can reduce the number of real trials needed, saving material costs and energy. This lecture will review solutions for the computational materials community, focusing on HTS in alloy design. The CALPHAD method facilitates the prediction of microstructure evolution by calculating phase distributions in multi-phase systems under various manufacturing conditions. We will demonstrate examples of application of the CALPHAD-based HTS to optimisation of heat treatment for additively manufactured metallic alloys.

Evgeniya Kabliman is a professor for knowledge-based digitalization in materials-oriented production at the University of Bremen and the director of the newly established area “Digital Technologies” at the Leibniz Institute for Materials Engineering – IWT. Prof. Kabliman applies computational methods in materials science and engineering to describe the material's behavior at multiple length scales. Her current research focuses on implementing a computational high-throughput screening approach combined with machine learning.