We are engaged in the investigation of graphene-based conductor materials, which consist of stacked layers of graphene flakes (see Figure 1). This material exhibits a range of microscopic material parameters, such as flake size, packing density, flake conductivity, and inter-layer conductivity, all of which exert influence on its macroscopic properties, particularly conductivity, which is our focal point of interest.

Initially, we employ nodal analysis to compute both in-plane and out-of-plane conductivities within a four-probe measurement setup (see Figure 1), with the values being dependent on various microscopic parameters [1,2]. Subsequently, we develop a surrogate model utilizing Gaussian process regression. The outcomes derived from the aforementioned nodal analysis serve as training data for the model, enabling rapid computation of in-plane and out-of-plane conductivities. Furthermore, the surrogate model can be refined by incorporating additional data points to minimize uncertainty.

Recently, the "Platform MaterialDigital" [3] has embarked on advancing digital twins within material science, specifically targeting the establishment of an ontology-based data storage system. This system aims to digitally represent (new) materials and their properties in correlation with crucial processing steps. We are actively involved in this endeavour and have initiated the development of an ontology specialized for graphene-based conductor materials (see Figure 2). This ontology is enriched with simulation data obtained from nodal analysis calculations, the surrogate model, and experimental data. It encompasses various aspects such as model parameters, preparation processes, measurement parameters, and more. Moreover, both the nodal analysis approach and the surrogate model can be integrated as workflows within the "Platform MaterialDigital", enhancing its functionality and utility within the realm of material science.

References
[1] L. Rizzi ACS Applied Materials & Interfaces, 2018, 10, 43088-43094
[2] L. Rizzi Computational Materials Science 2019, 161, 364-370
[3] www.materialdigital.de