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

Abstract Computational materials science increasingly benefits from data management, automation, and algorithm-based decision-making for the simulation of material properties and behavior. Experimental materials science also changes rapidly by incorporation of ‘machine learning’ in materials discovery campaigns. The benefits including automation, reproducibility, data provenance, and reusability of managed data, however, are not widely available in the experimental domain. We present an implementation of an Active Learning loop with an interface to an experimental measurement device in pyiron as a demonstrator how to combine experimental and simulated data in one framework. Apart from the acceleration provided through active learning, additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as predictions based on text mining using correlations in word embeddings. With data from all domains in the same framework, an untapped potential for the acceleration of materials characterization and materials discovery campaigns becomes available.