We present an overview over our digitalization efforts related to graphene-based macro materials (GBMs). These efforts are done in close interaction with the platform MaterialDigital (PMD), utilizing the tools offered by the PMD [1, 2].

GBMs are fabricated using a blade coating technique, followed by an annealing step, and measured with respect to their electrical conductivity [3]. We have developed suitable parsers as well as an app to handle and store the measured data, which can be used for further post-processing. As an example, we analyse the experimental data regarding the conductivity of GBMs. The experimental data come with a larger number of features, and we illustrate via feature extraction how a smaller number of features can be used to analyse the overall material performance.

In parallel to the experiments, a network model is used to study GBMs computationally [4], which we already applied previously to explain measurements [3, 5]. Since computations are faster than experiments, we can sample a larger parameter space and create more data for surrogate models. Consequently, we use the calculated data to train a Gaussian process model. Our computational workflow is optimized and standardized using the workflow manager pyiron [1].

All our data are finally collected in an ontology, which we have specifically developed to describe GBMs, and which is based on the PMD core ontology [2]. This domain-specific ontology contains not only computed and measured material properties, but also information about the various processing steps in the experiment.

References
[1] S. Bekemeier et al. Advanced Engineering Materials, 2025, 2402149.
[2] B. Bayerlein et al., Materials & Design, 2024, 237, 112603.
[3] L. Niemann et al. Diamond and Related Materials, 2024, 147, 111310.
[4] L. Rizzi et al. ACS Applied Materials & Interfaces, 2018, 10, 43088.
[5] M. Stevens et al., Diamond and Related Materials, 2025, 153, 111989.