High-throughput computational screening of secondary alloys from scrap metal mixtures

Kurzfassung

A framework for high-throughput screening of alloys from scrap mixtures has been successfully implemented and the first mixtures have been selected for experimental evaluation. A random forest surrogate model allowed the prediction of more than 60 properties and significantly reduced the time required to obtain a comprehensive database. The experimental data provided input for validation and fine-tuning of the simulations and also provided microstructural information that could not be predicted by the simulation models used. The combined methodology presented has great potential to dramatically accelerate the innovation cycle.