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Computational materials science is increasingly benefitting from datamanagement, automation, and algorithm-based decision-making in controllingsimulations. Experimental materials science is also undergoing a change andincreasingly more `machine learning' is incorporated in materials discoverycampaigns. The obvious benefits include automation, reproducibility, dataprovenance, and reusability of managed data, however, is not widely available.We demonstrate an implementation of a Gaussian Process Regression directlycontrolling an experimental measurement device in pyiron, a framework designedfor high-throughput simulations, as a first step to increasingly combineexperimental and simulated data in one framework. With data from both in thesame framework, a heretofore untapped and much-needed potential for theacceleration of materials characterization and materials discovery campaignsbecomes available.

Computationally accelerated experimental materials characterization -- drawing inspiration from high-throughput simulation workflows