Coordination: Alfred Ludwig, Jan Rossmeisl

The joint project integrates high-throughput calculation of electrochemical properties of SAA with their high-throughput experimental exploration. A01 develops the theory of CCSS electrocatalysis based on DFT simulations and uses it to predict SAA with high electrocatalytic performance. Understanding of how SAA determine catalytic activity will be developed by comparing predictions based on atomic scale mechanistic studies to experiments. A01 provides noble metal CCSS thin film systems in large compositional ranges in form of materials libraries synthesised by combinatorial sputtering as one part of the CRC’s material platform. Their properties are determined using high-throughput characterisation, providing a dataset of the single-phase solid solution ranges of CCSS systems and their electrochemical activity (with C01, C02) to the CRC. 

Coordination: Alfred Ludwig, Dierk Raabe

A02 designs the microstructure of noble metal CCSS (with preselected systems to start with and based on results of A01) to enable atomic-scale experiments. The aim is to obtain smooth quasi-single-crystalline films with up to micrometre-sized grains. The crystallographic orientation of these grains is determined, enabling the orientation-dependent investigation of CCSS properties. Furthermore, the defect structure of the quasi-single-crystalline films will be altered, enabling elucidation of the effects of defects on electrocatalytic properties (with all B and C projects). A02 provides the second part of the CRC’s material platform. SFB.

Coordination: Ralf Drautz

Multinary alloys rarely are ideal solid solutions. In the volume short-range order may be significant. At surfaces, segregation may completely change the composition and order of the first few atomic layers. We will simulate segregation and ordering at CCSS surfaces with accurate and fast machine learning interatomic potentials. The results of the simulations will be fed back into alloy screening and optimization in project A01 in order to generate the best CCSS surface for catalytic processes.

Coordination: Jörg Neugebauer

Project A04 aims to provide a highly realistic description of key catalytic reactions on selected CCSS surfaces through the use of ab initio molecular dynamics (AIMD) simulations. These simulations will explicitly take into account the impact of electrified surfaces, large electric fields in the interface region, potentiostat control, polarization of the electrolyte at the interface, and water co-adsorption and will thus allow us to understand and quantify how these mechanisms impact adsorption behaviour and electrocatalytic activity of CCSS surfaces. This information will be critical for the high-throughput computational activities in A01 and for interpreting the electrochemical experiments performed in the C projects.

Coordination: Markus Stricker

A05 is situated at the intersection of all CRC projects that generate materials data, both from experiment and simulation, to fuse the information contained in disparate data sources to leverage synergetic effects and offer a complete and accurate representation of the knowledge contained in the combined data from all projects. This is achieved by (i) assistance with and provisioning of pre- and post-processing tools related to the common data infrastructure (cf. INF project) and (ii) analysing the information contained in the combined dataset to (iii) provide guidance to the next set of measurements or simulations to minimise knowledge uncertainty and (iv) formulate design principles for CCSS based on machine learning algorithms for catalysis.

Coordination: Maribel Acosta

A06 will establish a CCSS knowledge graph (KG) to represent all data used and generated in the CRC in a uniform way. The KG will contain data about experiments, theory, and simulations that can be accessed and interrogated by experts as well as analytical tasks. For this, A06 investigates suitable representations of CCSS and SAA to accelerate their detailed understanding. To populate the KG, A06 develops algorithms to integrate the heterogeneous data sources of the CRC. In addition, A06 incorporates into the KG complex knowledge in the form of rules generated by experts or machine learning models. Lastly, A06 provides efficient access to the KG. The techniques devised in A06 are deployed through the INF project to serve all other projects.