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    Machine-Learned Force Fields for Lithium Manganese Oxide Cathode Materials
    Lithium manganese oxides (LMO) are promising cathode materials for lithium batteries. The transport properties crucial for battery performance are heavily dependent on the electronic structure and polarons, coupling the electronic structure with lattice distortions. While advanced X-ray scattering methods can, in principle, be used to investigate such properties, detailed knowledge of LMO is necessary to guide and analyze such experiments. Computational approaches can contribute significantly here. Traditionally, this requires sophisticated quantum mechanics calculations. However, accessible system sizes and time scales are limited using such methods. Thus, they often offer only an insufficient picture of the charging and discharging process. The development and training of a machine-learned force field (MLFF) for LMO has the potential to complete this picture. However, it requires a novel class of MLFFs that treat explicit charges in a way that accounts for long-range electrostatic interactions. Based on the charge equilibration (QEq) approach implemented in our ELECTRODE software package, we envisage incorporating charges into the MLFF. It is necessary to describe the short- and long-range order of extended polaronic structures in LMO. This study will support X-ray experiments to enable the tracking of lithium transport in LMO. Therefore, combining atomistic simulations with experiments would provide novel insights into this material and technological advancements.
      24
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    EXC 3120 BlueMat - Research Topic D4: Photo- and Electrocatalysis in Hierarchical Matter
    Nature often performs complex catalytic reactions in confined spaces and hierarchical structures with the use of enzymes and membranes. In RT D4, we will develop Blue Materials that emulate the confined, hierarchical environments of biological systems to achieve unprecedented levels of catalytic performance. By combining material synthesis, characterization, and catalytic testing, we will leverage multiscale structures for optimized catalysis. We will unravel the complex behavior of reactants, including water, in confined spaces and their interactions with nanostructured materials. The key questions are: How do properties of water in confined spaces affect water- and proton-dependent reactions? How does confined space affect the EDL, the reaction activity and selectivity, and the properties of catalytic materials, reactants, and products? And how can we harness these properties to achieve more selective and durable catalysts?
      24
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    EXC 3120 BlueMat - Cross Area Imaging
    To unravel the unique physics and chemistry of water-driven materials, the CA Imaging aims to achieve groundbreaking innovations in three primary focus areas: (1) extreme temporal resolution imaging, (2) multiscale imaging spanning 10 orders of magnitude in length and 16 in time, and (3) correlative image analysis that integrates these diverse data sources to uncover the role and structural dynamics of water in multiscale structured matter with an unprecedented accuracy and comprehensiveness. To this end, BlueMat will leverage the unparalleled infrastructure at the EuXFEL and PETRA III at DESY in Hamburg, offering exceptional opportunities for correlative in-operando characterization. Extreme temporal resolution imaging will be realized by advancing MHz X-ray multi-projection imaging (MHz XMPI), stroboscopic pump-probe X-ray diffraction (XRD), and X-ray photon correlation spectroscopy (XPCS) at X-ray free electron lasers. This will enable the visualization of atomic interactions, molecular mobility, and flow dynamics down to femtosecond and picosecond timescales. Leveraging a uniquely comprehensive combination of methods, we will image water across a wide range of scales. For instance, we will observe macroscale mean flow using magnetic resonance (MR), ?m- to sub-?m-scale flow patterns via synchrotron radiation nano computed tomography (SRnCT), and detailed processes at the nm-scale using scanning and transmission electron microscopy (SEM/TEM). Finally, CA Imaging will pioneer computational and deep learning techniques to achieve a step change in throughput and resolution of characterization methods, enabling truly correlative image analysis. The qualitative and quantitative data collected in CA Imaging are crucial for model validation in CA Modeling and calibration in CA Exploitation & Experiencing. By making these unique methods available to all young researchers in the cluster, CA Imaging further contributes to the highly advanced education they will receive.
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    EXC 3120 BlueMat - Research Topic D2: All-Water Redox Batteries
    D2 focuses on pioneering new battery solutions using water-based electrolytes and elucidating the critical interactions between water and aqueous electrolyte components at electrified interfaces. We will investigate the interactions between novel electrode materials and water within the context of battery technology. Our approach integrates advanced in-situ and in-operando characterization of material interfaces with modeling to bridge theoretical predictions and experimental observations. Such batteries use zinc metal anodes and cathodes with materials capable of intercalating anions. Dual ion batteries (DIBs) have a high energy density and relatively low cost, making them promising for use in various devices. By understanding the dynamic interactions of water within the pores of hierarchically structured cathode materials combined with layered double hydroxides (LDH), we aim to gain insights into the electrochemical performance and stability of these materials. On the anode side, we will explore the dynamic interactions of water during charge/discharge cycles and the effects of electrolyte additives on Zn dendrite formation. Key scientific questions include: What are new efficient electrolyte additives capable of controling charge/discharge processes on metallic Zn anodes? What is the mechanism at the atomistic scale? How can we develop the effective anion intercalation cathode assembled on hierarchically designed current collectors and open a pathway for the implementation of DIBs? What is the role of water in the ion-exchange processes and in the charge transfer within double hydroxide layers and in the interlayer spaces?
      57
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    Identification of optimal corrosion inhibitors for bare and PEO-coated magnesium alloy by combining machine learning and robotic testing
    The major goal of the proposed project is a shift of paradigm for the development of active anti-corrosion coatings using the example of the specific magnesium alloy AZ31. We will demonstrate how machine learning (ML) combined with robotic testing can assist the identification of optimal corrosion inhibitors for bare and PEO-treated magnesium. Highly automated robotic testing will be used to create a large basis of experimental data characterizing the corrosion inhibition efficiency (IE) of a large number of different chemical compounds for AZ31. In parallel, we will develop a machine learning architecture that can benefit from the collected data to predict the corrosion IE of chemical compounds, paving the way towards computer-aided identification of promising corrosion inhibiting compounds. In doing to, our ML architecture will use also data from density functional theory (DFT) calculations, which help to reduce the amount of experimental data required for meaningful predictions. Particularly promising magnesium corrosion inhibitors identified in the course of this project will be examined in depth. Their corrosion inhibition mechanisms will be uncovered by employing a combination of electrochemical techniques accompanied by DFT calculations. The approach pursued in this project will pioneer a new paradigm for the automated and accelerated enhancement of corrosion protection, allowing the accurate and fast identification of the most suitable corrosion inhibitors for a given material from a large experimental database of potentially available corrosion inhibiting compounds.
      31
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    CRC 1615 - Project A03: Surface-functionalised nanoporous solids: Towards responsive materials for SMART reactors with adjustable fluid adsorption, transport and molecular hydrogen sensorics
    This project aims at the development of a multifunctional porous material system with controllable adsorption, fluid transport as well as integrated hydrogen sensing. The material design employs a hybrid sandwich structure integrating nanoporous silicon (np-Si) and nanoporous gold (np-Au) with tailorable pore size and surface functionalisation of pore walls. The stimuli-responsive np-Si/np-Au hybrid will be evaluated as a building block for SMART reactors in form of (i) protective layers for enzymatic reactions in mixed solvents and (ii) selective adsorbents with dual size-exclusion and specific modes of action.
      88
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    SPP 2402: Extrapolative digital greybox models for describing and predicting the macroscopic system behavior of TiAlN-coated cutting tools
    Tools with a hard coating make up the majority of cutting tools used today. They protect the substrate from abrasive wear, increase chemical resistance and reduce the coefficient of friction, resulting in increased tool life during turning and milling operations. Various empirical or physically based models exist for the estimation of tool life. Since the properties of these coatings depend on a large number of manufacturing parameters, no consistent models exist to predict the wear behavior of hard coatings. The objective of the proposed research project is to gain knowledge about the tribological cause-and-effect relationships of the wear behavior of coated cutting tools in interaction with the workpiece material and the process parameters under consideration of the particular mechanical, chemical-structural and tribological properties of the coating and to map this knowledge into a model. Therefor greybox models shall be used. These models are rely on artificial neural networks (blackbox) that use training data based on process parameters, measurements and metadata from machining tests. Physical and empirical models for wear rate prediciton and remaining tool life are additionally integrated into these models (whitebox). This allows the model, with the limited scope of training data due to the high experimental effort, to predict physically meaningful solutions. In machining tests, which are partially automated by integrating force, temperature, structure-borne sound and surface measurement techniques as well as microscopy in the workspace of a CNC lathe, a continuously growing database is created for the simultaneous model building. In order to minimize the experimental effort and thus the material and energy costs, the next experimental parameters are determined in each case on the basis of the current training state of the model using Active Learning. The recorded measurement and metadata are available in a wide variety of formats, such as image data, continuous measurement or discrete measurement points. They are processed and stored with their metadata in an digital lab book. Subsequently, the data is homogenized and reduced to obtain a balanced data set for training the neural networks. For final validation, blind tests, with cutting parameters and coating unknown to the model, are performed to confirm the interpolation and extrapolation capabilities of the greybox model.
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