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.

Here we will 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 [2]. 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 the acquisition of experimental data from the magnetization measurements in a VSM magnetometer (QD MPMS)  and the automatic evaluation of magnetic phase transitions from the measured data. This workflow is further enhanced by a second workflow, evaluating the given alloy using DFT and spin dynamics simulations to acquire simulational data about the magnetic phase transition of said alloy for comparison to the experimental data.

This workflow eliminates the need for manual "translation" between tools (e.g. measurement devices, data processing, and simulational codes), enabling easy concatenation of such tools and their exchange among researchers. It empowers, for example, experimentalists to enhance their knowledge generation process by incorporating theoretical simulations into their work.

Combining both developments enables the automation of the entire research 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.

[1] Jan Janssen, Sudarsan Surendralal, Yury Lysogorskiy, Mira Todorova, Tilmann Hickel, Ralf Drautz, and Jörg Neugebauer. ‚pyiron: An integrated development environment for computational materials science‘. In Comput. Mater. Sci., 2019, doi:10.1016/j.commatsci.2018.07.043.
[2] B. Bayerlein u. a., „PMD Core Ontology: Achieving semantic interoperability in materials science“, Materials & Design, Bd. 237, S. 112603, Jan. 2024, doi: 10.1016/j.matdes.2023.112603.