Process modelling and high-throughput thermochemical calculations using ChemApp for Python

GTT-Technologies’ ChemApp for Python was developed to provide a powerful, easy to use interface to ChemApp for a programming language highly popular with scientists and engineers. It is used, for instance, by GTT to develop program modules such as the CALPHAD Optimiser for the FactSage™ software, by customers to move from interactive FactSage calculations to perform versatile scripting with Python, and by GTT and its partners in research projects in the area of materials informatics. Computational thermochemistry is fundamental for advancing sustainable metallurgy and creating new alloy compositions for engineering applications. Materials informatics involves handling vast amounts of data and complex workflows. GTT’s approach uses ChemApp for Python and the FactSage thermodynamic databases to design recyclable alloys from the start, incorporating a higher percentage of scraps while aiming to simplify the workflows to simulate material design steps. Challenges arise due to recycling scraps, introducing more elements for consideration. CALPHAD-based databases accurately cover materials from primary metallurgy, but additional data for minority and critical elements is crucial for precise computational modelling. GTT combines machine learning-based ab-initio databases with traditional CALPHAD databases to cover the complete chemical space with appropriate accuracy. The design of a hardfacing alloy through a high-throughput materials informatics approach is used as a demonstrator of the current possibilities.