Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

TL;DR

The paper addresses the challenge of organizing and accelerating magnetocaloric materials research by digitizing the entire process chain through the DiProMag OTTR-based ontology. It introduces a template-driven, ontology-centric framework that unifies synthesis, characterization, and prototyping data, and couples this with automated data pipelines and a two-branch workflow (DFT/MCMC simulations and automated experiments) to compute the magnetocaloric metric $\Delta S$. It also explores machine-learning approaches on knowledge graphs, including physical knowledge in vector spaces and KG completion with literals, to guide discovery and predict missing facts. Across bulk MnNiGe-based systems, thin-film Co$_2$CrAl, and prototyping workflows, the framework demonstrates integrated data capture, standardized evaluation of magnetic phase transitions, and automated, FAIR-enabled sharing of tools and results. Overall, the work provides a practical pathway to accelerate magnetocaloric materials discovery by bridging experimental and computational methods within a reusable, community-accessible digital infrastructure.

Abstract

Refrigeration based on the magnetocaloric effect (MCE) can contribute to energysaving, environmentally friendly cooling in private households, or industrial application. The cooling is based on the reversible heat release or uptake during a phase-transformation of the materials that can be controlled by a magnetic field. This process could replace conventional compression-based refrigeration, which often relies on environmentally harmful refrigerants. Here we show, how to digitalize the process chain for the synthesis, theoretical and experimental characterization, and prototypical application of magnetocaloric alloy. Different Heusler alloys are examined experimentally as model systems for potential application in magnetic cooling. OTTR templates are used for the acquisition and semantic representation of knowledge in the development of an ontology. The ontology, when combined with unstructured data, can be exploited to train a model that can then be used to predict missing facts, which can help to gain new insights and to generate new hypotheses. Furthermore, tools are developed that automate data acquisition into ontological structures and workflows are implemented that provide an easy-to-use theoretical and experimental evaluation of the MCE from first principles and raw data.

Figures (12)

• Figure 1: a) an example template library and an instance of a template. The terms p:Gadolinium and "Gadolinium"@it are bound to the template parameters ?name and ?label, respectively, to create an instance of the template, whose triples are shown underneath. b) Relations between the example templates. c) Template call hierarchy of the dpm:MeasurementMagneticProperties template.
• Figure 2: General overview of the DiProMag ontology structure.
• Figure 3: Vector space visualization of chemical elements, colored by element group. One can see that transition metals and lanthanides form clearly visible clusters.
• Figure 4: Vector space visualization of Heusler compounds colored according to in which columns of the periodic table the individual elements that make up the compound are found.
• Figure 5: (a) Temperature dependence of the magnetization (M) in the presence of different constant magnetic field (B) during heating and cooling for MnNi0.8Co0.2Ge0.97Al0.03. The OTTR template instance captures information about the magnetic phase transition temperatures ($T_C$) for heating and cooling and the thermal hysteresis $\Delta T_{hyst}$ for different magnetic fields $B$. (b) Entropy change ($|\Delta S|$) and associated reversibility ($|\Delta S_{rev}|$) as a function of temperature. The OTTR template instance captures relevant information of $|\Delta S|$, such as maximum entropy changes during heating and cooling, and the associated reversible entropy change.