Simon Bekemeier

 S. Nickel, O.-C. Yaman, T. Hilbig, C. Schröder

Hochschule Bielefeld – University of Applied Sciences and Arts, Bielefeld Institute for Applied Materials Science, Computational Materials Science and Engineering, Germany

Accelerating Knowledge Generation and Materials Discovery through Automated Digitalization Workflows

Digitalization and increasing computational capacities offer immense opportunities to accelerate knowledge generation and materials discovery. However, challenges persist in terms of unstructured or non-standard data formats and manual execution of data processing and simulations, hindering the realization of the full potential of digitalization.

In this poster, we present two key developments from the DiProMag project. Firstly, we showcase the infrastructure and current advancements in automating data acquisition and processing.

Our developed pipeline integrates an automatic data acquisition system, the eLabFTW electronic lab notebook for central storage, the pyiron workflow framework[1] for automated simulations, and transformation of data into an ontological structure compatible with the PMD core ontology.
This automated data acquisition pipeline facilitates a seamless transition from analog to digital processes, minimizing the negative impact of digitalization on researchers and simplifying the adoption of digital tools.

Secondly, we present a workflow example that encompasses multiple simulation steps, including DFT, spin dynamics simulations, and evaluation of magnetic phase transitions. This workflow eliminates the need for manual "translation" between tools, enabling easy concatenation of tools and the exchange of simulation tools among researchers. E.g. it empowers experimentalists to enhance their knowledge generation process by incorporating theoretical simulations into their work.

Combining both developments enables the automation of the entire workflow, from data acquisition in laboratories to subsequent calculations, simulations, and post-processing, culminating in the integration of results into ontological structures.

This automation not only facilitates the utilization of digital tools and computational methods but also streamlines the adoption of FAIR data and workflows. By alleviating technical challenges, researchers can leverage the full potential of digitalization to accelerate knowledge generation and materials discovery