TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding with bonding analysis N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed. T2 - Seminar of the Department of Chemistry at Imperial College London CY - Online meeting DA - 20.02.2024 KW - Automation KW - Magnetism KW - Bonding Analysis KW - Machine Learning PY - 2024 AN - OPUS4-59546 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-throughput Approaches for Materials Understanding and Design N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties.[1] Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available.[2,3] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale.[3,4] The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.[7–10] T2 - International Materials Science and Engineering Congress - MSE 2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Automation KW - High-throughput KW - Chemically Complex Materials KW - Thermal Properties KW - Phonons KW - Bonding Analysis PY - 2024 AN - OPUS4-61118 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-throughput approaches for materials understanding and design N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties.[1] Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available.[2,3] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale.[3,4] The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.[7–11] T2 - TCO 2024 - Transparent Conductive Oxides, Fundamentals and Applications CY - Leipzig, Germany DA - 23.09.2024 KW - Automation KW - Materials discovery KW - Machine Learned Interatomic Potentials KW - Workflows KW - Chemically Complex Materials KW - Bonding Analysis PY - 2024 AN - OPUS4-61153 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-Throughput Approaches for Materials Understanding and Design N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed. T2 - International Symposium on Computational Structure Prediction and Advanced Materials CY - Louvain-la-Neuve, Belgium DA - 22.08.2024 KW - Automation KW - High-throughput KW - Magnetism KW - Phonons KW - Machine learning KW - Chemically complex materials PY - 2024 AN - OPUS4-60870 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - New opportunities through an efficient combination of machine learning and high-throughput computing N2 - The underlying data is crucial for machine learning (ML) tasks.[1] Ab initio data is often used as a target (occasionally as features[2]). High-throughput calculations and automation make it possible to generate such data as efficiently as possible and with uniform standards.[3,4] The high-throughput data of the Materials Project has recently been used to train new foundation interatomic potentials[5]. Several high-throughput frameworks have been developed in recent years. This presentation will introduce the atomate2/jobflow[3,4] framework that is used and developed by researchers around the Materials Project. In the future, it will be used to generate the data for the Materials Project. Atomate2 allows both the use of DFT and ML potentials within one framework. Building upon this framework, we have developed a package with the possibility to train and benchmark ML potentials automatically. Currently, the package can be used to train ML interatomic potentials sufficient to predict harmonic phononic properties of materials. Typical foundation models in this area now achieve acceptable results but are still far from routinely replacing DFT.[5,6] Building on the promising results from the ref where we explored data generation strategies for accurate phononic properties[7], we will present new fully automated workflows for training and benchmarking ML interatomic potentials with force predictions that are accurate enough to compute harmonic phonons in very good agreement with DFT for different crystal structures of the same composition. T2 - GAP/(M)ACE Developers & Users Meeting 2024 CY - Berlin, Germany DA - 17.09.2024 KW - Automation KW - Workflows KW - Machine Learned Interatomic Potentials KW - Thermal Properties KW - Bonding Analysis KW - Chemically Complex Materials PY - 2024 AN - OPUS4-61099 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-Driven Approaches for Materials Understanding and Design N2 - Data-Driven Approaches for Materials Understanding and Design Analyzing the crystal structure (including coordination environments, bond distances, and angles) and chemical bonding are standardly used in chemistry to understand material properties. In this talk, I will show how we can use this information in machine learning of material properties. For this purpose, I will present our software tools ChemEnv[1] for analyzing coordination environments and for automation of orbital-based bonding analysis with LOBSTER[2] (LobsterPy[3] and density-functional theory workflows in atomate2[4]). Enabled by these software tools, we have built interpretable machine-learned models for magnetic and vibrational properties that allow us to test and further develop intuitive rules.[5,6] T2 - Institutskolloqium Institut für Anorganische Chemie und Kristallographie CY - Leipzig, Germany DA - 23.10.2024 KW - Automation KW - Bonding Analysis KW - Materials Design KW - Chemically Complex Materials KW - Phonons KW - Thermal Properties KW - Synthesizability KW - Machine Learning PY - 2024 AN - OPUS4-61445 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-throughput and data-driven approaches for materials understanding and design N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties.[1] Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available.[2,3] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale.[3,4] The lecture will demonstrate how our tools, which assess local atomic environments and perform automatic bonding analysis, help to develop new machine-learning models and a new intuitive understanding of materials.[5,6] Beyond this, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.[7–11] The focus will be especially on the interplay of DFT and machine-learned interatomic potentials will present new fully automated workflows for training and benchmarking machine-learned interatomic potentials with force predictions accurate enough to compute harmonic phonons in very good agreement with DFT. Semi-automated fine-tuning of existing ML models for phonon properties will also be discussed. T2 - XXIV Chilean Physics Symposium CY - Temucu, Chile DA - 20.11.2024 KW - Automation KW - Machine Learning KW - Chemically Complex Materials KW - Machine-Learned Interatomic Potentials KW - Finetuning PY - 2024 AN - OPUS4-61749 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Neue Wege in der Materialforschung: Zusammenspiel von Hochdurchsatz-Simulationen und maschinellem Lernen N2 - Dies ist meine Antrittsvorlesung an der Friedrich-Schiller-Universität Jena, die im Rahmen der BAM-Universität-Jena-Kooperation entstanden ist. Hier stelle ich die Materialinformatik und unsere Forschung im speziellen vor. T2 - Antrittsvorlesung an der Friedrich-Schiller-Universität Jena CY - Jena, Germany DA - 20.01.2025 KW - Automation KW - Ab initio KW - Machine learning KW - Phonons KW - Materialdesign KW - Materials Acceleration Platforms PY - 2025 AN - OPUS4-62437 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Materials design using chemical heuristics, workflows, and machine learning N2 - Bonds and local atomic environments are key descriptors of material properties, used to establish design rules and heuristics, and serve as descriptors in machine-learned interatomic potentials and the general machine learning of material properties. Software implementations such as ChemEnv and LobsterEnv identify local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis (here using Crystal Orbital Hamilton Populations as computed with LOBSTER). Fully automated workflows and analysis tools now enable large-scale quantum-chemical bonding analysis. The first part of the lecture will demonstrate how these tools help develop new machine-learning models and intuitive understandings of material properties. New universal machine-learned interatomic potentials, such as MACE-MP-0, have been developed. The second part of the lecture will showcase how these potentials, combined with DFT, can accelerate research. It will focus on the interplay between DFT and machine-learned interatomic potentials, presenting automated workflows for training, fine-tuning, and benchmarking these potentials, implemented in our software autoplex. Additionally, it will show how to train new interatomic potentials from scratch by exploring potential energy surfaces, with the potential to enhance current universal machine-learned potentials. The lecture will also discuss the trend toward automation in computational materials science and our recent contributions. T2 - FAIRmat Seminar CY - Berlin, Germany DA - 05.06.2025 KW - Automation KW - Machine learning KW - Synthesizability KW - Sustainable materials design KW - High-throughput KW - Bonding analysis KW - Materials acceleration platforms PY - 2025 AN - OPUS4-63315 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Automated Bonding Analysis with LobSTER and ATOMATE2 N2 - The talk introduced automated bonding analysis with the programs LobsterPy and atomate2. I especially emphasized how bonding descriptors can be used for materials design. T2 - Lobster Bonding Analysis School CY - Berlin, Germany DA - 11.06.2025 KW - Automation KW - Bonding Analysis KW - Materials Design KW - High throughput KW - Ab initio KW - Thermal conductivity PY - 2025 AN - OPUS4-63420 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Lobster workflow and applications N2 - This talk introduced bonding analysis to the participants of the school including several examples of its usefulness. Additionally, I talked in detail about the workflows related to bonding analysis within the atomate2 workflow library. I then showed how the workflow was applied to build a very large bonding data database that can now be used for machine learning of materials properties. T2 - "Automated ab initio workflows with Jobflow and Atomate2" CECAM Flagship school CY - Lausanne, Switzerland DA - 17.03.2025 KW - Automation KW - Materials design KW - Machine learning KW - Thermal conductivity KW - Inorganic materials KW - High-throughput PY - 2025 AN - OPUS4-62948 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Phonon workflow and applications N2 - This talk introduced the participants to phonons and how they are typically computed. Then, I introduced the participants to the harmonic phonon, the Grüneisen, and the quasi-harmonic workflow that allows the computation of properties related to phonons. I also had detailed examples of how these workflows can be used in practice. Beyond this, I showed how these workflows can be used to automatically benchmark interatomic potentials and use them for the development of such potentials. T2 - "Automated ab initio workflows with Jobflow and Atomate2" CECAM Flagship school CY - Lausanne, Switzerland DA - 17.03.2025 KW - Automation KW - Phonons KW - Thermal Conductivity KW - Machine Learning KW - Software Development KW - Machine Learned interatomic Potentials PY - 2025 AN - OPUS4-62949 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-Driven Materials Design N2 - Implementations and algorithms, such as ChemEnv and LobsterEnv, for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are now available. Fully automatic workflows and analysis tools have been developed to use quantumchemical bonding analysis on a large scale. The first part of the lecture will demonstrate how our tools, which assess local atomic environments and perform automatic bonding analysis, help develop new machine-learning models and a new intuitive understanding of material properties. Many new universal machine-learned interatomic potentials, such as MACE-MP-0, have been developed. The second part of the lecture will showcase how these potentials, in combination with DFT, could significantly accelerate our research. The focus will be on the interplay of DFT and machine-learned interatomic potentials, presenting new fully automated workflows for training, finetuning, and implemented benchmarking these potentials, in our software autoplex (https://github.com/autoatml/autoplex). Additionally, I will show how to train new interatomic potentials from scratch by exploring potential energy surfaces extensively, offering a method to enhance current universal machine-learned potentials. Beyond this, the general trend toward automation in computational materials science and some of our recent contributions will be discussed. T2 - AI4AM2025 CY - Donostia, Spain DA - 08.04.2025 KW - Automation KW - Machine Learning KW - Chemically Complex Materials KW - Thermal Conductivity KW - Bonding Analysis KW - Workflows KW - Machine Learned Interatomic Potentials PY - 2025 AN - OPUS4-62950 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Naik, Aakash T1 - Harnessing quantum chemical bonding analysis descriptors for material property predictions N2 - Examining the bonding between their constituent atoms in crystalline materials has played a vital role in understanding material properties.[1–4] For instance, low thermal conductivity in materials is typically attributed to its anharmonicity, which has been reported to arise from strong antibonding interactions and local environment distortions.[5–7] The bonds in the material are often quantified in terms of bond strength and can be extracted from crystalline materials using density-based[8], energy-based[9], and orbital-based[10] methods. LOBSTER[11] is a program that relies on an orbital-based method to extract such bonding information by projecting the plane wave-based wave functions of modern density functional theory computations (DFT) onto a local atomic orbital basis. Since our goal was to use bonding analysis descriptors for material property predictions, we needed to first systematically generate large quantities of bonding analysis data. To streamline this process, we have developed a user-friendly workflow[12], which is now also part of the atomate2[13] package that can generate bonding information data extracted using the LOBSTER program for crystalline materials. This workflow requires only the structure as input from the user. Employing this workflow, we have generated for ~13000 crystalline compounds such bonding analysis data. To create new descriptors from these data, we use our package LobsterPy.[14] The curated descriptors span different types, including statistical representations of bonding characteristics for traditional ML algorithms (e.g., random forests), textual descriptions for large language models (LLMs), and structure graphs for graph neural networks (GNNs). These descriptors are then tested by employing them in several state-of-the-art ML algorithms and architectures to predict the mechanical, vibrational, and thermal properties of crystalline materials. Through this work, we are not only able to demonstrate how one can enhance the model’s predictive accuracy[15] by incorporating quantum chemical bonding-based descriptors alongside typical composition and structure-based descriptors but it also aids in uncovering relationships between bonding and materials properties on a larger scale, which was not possible before. T2 - MRS SPRING 2025 CY - Seattle, WA, USA DA - 07.04.2025 KW - Bonding analysis KW - Machine learning KW - Materials Descriptors PY - 2025 AN - OPUS4-63001 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waske, Anja T1 - A unique Authenticator for additively manufactured parts N2 - Components produced using additive manufacturing can be marked for unique identification and secure authentication [1,2]. Serial numbers and machine-readable codes can be used to identify the component, and link digital product-related data (i.e., a digital product passport) to the actual components. The most prevailing solution consists of local process manipulation, such as printing a quick response (QR) code [3] or a set of blind holes on the surface of the internal cavity of hollow components. However, local manipulation of components may alter the properties, and external tagging features can be altered or even removed by post-processing treatments. This work therefore aims to provide a new methodology for identification, authentication, and traceability of additively manufactured (AM) components using microstructural features that are unique to each part. X-ray computed tomography (XCT) was employed to image the microstructural features of AlSi10Mg parts. Based on size and geometry, the most prominent features were selected to create a unique digital authenticator. We implemented a framework in Python using open-access modules that can successfully create a digital object authenticator using the segmented microstructure information from XCT. The authenticator is stored as a QR code, along with the 3D information of the selected features. T2 - DGM Additive Berlin 2024 CY - Berlin, Germany DA - 12.06.2024 KW - Additive Fertigung KW - Authentifizierung KW - Mikrostruktur PY - 2024 AN - OPUS4-60957 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gupta, Kanhaiya T1 - Microstructural fingerprinting of additively manufactured components prepared by PBF LB/M N2 - Additive manufacturing (AM) is rapidly emerging from rapid prototyping to industrial production [1]. Thus, providing AM parts with a tagging feature that allows identification, like a fingerprint, can be crucial for logistics, certification, and anti-counterfeiting purposes since nearly any geometry can be produced by AM with stolen data or reverse engineering of an original product. However, the mechanical and functional properties of the replicated part may not be identical to the original ones and pose a safety risk [2]. Several methods are already available, which range from encasing a detector to leveraging the stochastic defects of AM parts for the identification, authentication, and traceability of AM components. The most prevailing solution consists of local process manipulation, such as printing a quick response (QR) code [3] or a set of blind holes on the surface of the internal cavity of hollow components. Local manipulation of components may alter the properties. The external tagging features can be altered or even removed by post-processing treatments. Integrating electronic systems [4] in AM parts can be used to identify and authenticate components with complex or customized geometries. However, metal-based AM, especially in powder bed fusion (PBF-LB/M) techniques, has a strong shielding effect that interferes with the communication between the reader and the transponder. Figure 1: Selection of the few most prominent pores sorted according to decreasing volume that are suitable for tagging and authentication. Our work aims to provide a new methodology for the identification, authentication, and traceability of AM components using microstructural feathers in AM components without altering their properties. Further, we set various benchmark points that can be used in generating the fingerprints for both identification and authentication. This can help digitalize traceability information and tagging features via the link between the physical and cyber worlds through a deeper understanding of the printed object-tag-virtual twin integration. T2 - MSE Konferennz CY - Darmstadt, Germany DA - 24.09.2024 KW - Fingerprint KW - Additive Manufacturing KW - Computed tomography PY - 2024 AN - OPUS4-62286 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waske, Anja T1 - A unique authenticator for additively manufactured parts derived from their microstructure N2 - In the field of additive manufacturing, the ability to uniquely identify and authenticate parts is crucial for certification, logistics, and anti-counterfeiting efforts. This study introduces a novel methodology that leverages the intrinsic microstructural features of additively manufactured components for their identification, authentication, and traceability. Unlike traditional tagging methods, such as embedding QR codes on the surface [1] or within the volume of parts, this approach requires no alteration to the printing process, as it utilizes naturally occurring microstructural characteristics. The proposed workflow [2] involves the analysis of 3D micro-computed tomography data to identify specific voids that meet predefined identification criteria. This method is demonstrated on a batch of 20 parts manufactured with identical process parameters, proving capable of achieving unambiguous identification and authentication. By establishing a tamper-proof link between the physical part and its digital counterpart, this methodology effectively bridges the physical and digital realms. This not only enhances the traceability of additively manufactured parts but also provides a robust tool for integrating digital materials, parts databases, and product passports with their physical counterparts. T2 - Artificial Intelligence in MSE CY - Bochum, Germany DA - 18.11.2025 KW - Authentication KW - Additive Manufacturing KW - Non-destructive testing PY - 2025 AN - OPUS4-65204 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hickel, Tilmann T1 - Interoperable workflows combining software tools along the process chain of materials N2 - Material science problems have intrinsically multiscale and multiphysics characters, and require us to employ a combination of methods on different time and length scales to resolve critical features. Normally creating workflows that connect data in multiple scales and various methods is a cumbersome task. Pyiron, an integrated development environment (IDE) for material science, contains modules for the atomistic as well as continuum scale that make a seamless connection possible. To this end, it provides a high-level coherent language in a unified workflow platform to study materials, for example, with LAMMPS simulations in the same framework as crystal plasticity codes such as DAMASK. The latter is integrated such that all required input files can be generated, the analysis can be executed and the routines of the DAMASK post-processing library can be employed. One example of a multi-scale workflow is the simulation of a multi-stage cold rolling process using pyiron. As with every DAMASK simulation, pre-processing includes the creation of the representative volume element and the material definition. It contains elastic properties that are obtained from LAMMPS calculations. To simulate the actual rolling process, the rolling subclass was implemented in the pyiron-job class. Since the total height decrease in technical rolling processes can be very large, the implementation provides for optional regridding between rolling passes. This is also required for the coupling with OpenPhase for simulating recrystallization using pyiron. To ensure interoperability between evaluation and simulation tools, work was done on a semantic representation of pyiron workflows. T2 - MSE Conference 2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Steel KW - Workflows KW - Multiscale simulation KW - Ontology PY - 2024 AN - OPUS4-62728 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Miliūtė, Aistė A1 - George, Janine A1 - Mieller, Björn A1 - Stawski, Tomasz T1 - ZrV2O7 negative thermal expansion (NTE) material N2 - Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material that exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). Therefore, it can be used to create composites with controllable expansion coefficients and prevent thermal stress, fatigue, cracking, and deformation at interfaces. We implement interdisciplinary research to analyze such material. We study the influence of the synthesis methods and their parameters on the sample's purity, crystallinity, and homogeneity. Moreover, we implement ab initio-based vibrational computations with partially treated anharmonicity in combination with experimental methods to follow temperature-induced structural changes and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder. T2 - SALSA Make and Measure Conference CY - Berlin, Germany DA - 13.09.2023 KW - NTE KW - Composites KW - TEM PY - 2023 AN - OPUS4-58367 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Okeke, Joseph T1 - In situ and operando imaging, spectroscopy and tomography of batteries N2 - The development of more powerful and more efficient lithium-ion batteries (LIBs) is a key area in battery research, aiming to support the ever-increasing demand for energy storage systems. To better understand the causes and mechanisms of degradation, and thus the diminishing cycling performance and lifetime often observed in LIBs, in operando techniques are essential, because battery chemistry can be monitored non-invasively, in real time. Moreover, there is increasing interest in developing new battery chemistries. Beyond LIBs, sodium ion batteries (NIBs) have gained increasing interest in recent years, as they are a promising candidate to complement LIBs, owing to their improved sustainability and lower cost, while still maintaining high energy density.[1] Initial phases of NIB commercialisation have occurred in the past year. However, for the widespread commercialisation of NIBs, there are still challenges that need to be overcome in developing optimized electrode materials and electrolytes. For the development of such materials and greater understanding of sodium storage mechanisms, solid electrolyte interface (SEI) formation and stability, and degradation processes, in operando methodologies are crucial. Among the techniques available for in operando analysis, nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI) are becoming increasingly used to characterize the chemical composition of battery materials, study the growth and distribution of dendrites, and investigate battery storage and degradation mechanisms. In situ and in operando 1H, 7Li and 23Na NMR and MRI have recently been used to study LIBs and NIBs, identifying chemical changes in Li and Na species respectively, in metallic, quasimetallic and electrolytic environment as well as directly and indirectly studying dendrite formation in both systems.[2-4] The ability of NMR and MRI to probe battery systems across multiple environments can further be complemented by the enhanced spatial resolution of micro-computed X-ray tomography (μ-CT) which can provide insight into battery material microstructure and defect distribution. Here, we report in operando 1H and 7Li NMR and MRI experiments that investigate LIB performance, and the identification of changes in the Li signal during charge cycling, as well as the observation of signals in both 1H and 7Li NMR spectra that we attribute to diminishing battery performance, capacity loss and degradation. Additionally, recent operando methodology are adapted and implemented to study Sn based anodes in NIBs. 23Na spectroscopy is performed to monitor the formation and evolution of peaks assigned to stages of Na insertion into Sn, while 1H MRI is used to indirectly visualize the volume expansion of Sn anodes during charge cycling. Battery operation and degradation is further explored in these NIBs, using μ-CT, where the anode is directly visualized to a higher resolution and the loss of electrolyte in the cell, during cycling is observed T2 - 17th International Conference on Magnetic Resonance Microscopy CY - Singapore DA - 27.08.2023 KW - Sodium ion battery KW - Lithium ion battery KW - NMR KW - MRI KW - Tomography PY - 2023 AN - OPUS4-58421 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -