Modern engineering materials have evolved from simple single phase materials to nano-composites that employ dynamic mechanisms down to the atomistic scale. As a consequence, efficient algorithms to accurately predict materials properties at realistic conditions require complex workflows coupling a multitude of different codes covering various scales and mechanisms. This makes the application and systematic development of such workflows time consuming and thus slow. To overcome these challenges, we have developed a python based framework named pyiron. This framework allows in a highly automated and user-friendly way to implement and benchmark complex simulation workflows to design materials with tailored properties. It further allows the user to easily upscale the workflows from interactive development to high-throughput simulation on powerful high-performance compute clusters. The flexibility and the predictive power of these automated workflows will be discussed and successful applications will. be given ranging from the design of ductile Mg alloys, over describing the finite temperature behavior of high entropy alloys, to the discovery of compositionally complex invar alloys.
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