Accelerated Materials Design and Materials Data Management at MCL

MCL is putting into practice a new paradigm for materials design based on an iterative process using Active Learning and a Bayesian optimization. In this context, we have built up the materials acceleration platform ALPmat (Active Learning Platform for materials design), which serves as infrastructure for data management and to run workflows for targeted material optimization based on inverse design algorithms. We present the new materials design process together with the architecture of the ALPmat. Moreover, we demonstrate the functionality of ALPmat for the optimization of bainitic steels in terms of yield strength and uniform elongation and present the related method development in terms of workflows, data management, and modeling.