TY - INPR A1 - Ganose, Alex A1 - Sahasrabuddhe, Hrushikesh A1 - Asta, Mark A1 - Beck, Kevin A1 - Biswas, Tathagata A1 - Bonkowski, Alexander A1 - Bustamante, Joana A1 - Chen, Xin A1 - Chiang, Yuan A1 - Chrzan, Daryl A1 - Clary, Jacob A1 - Cohen, Orion A1 - Ertural, Christina A1 - George, Janine A1 - Gallant, Max A1 - George, Janine A1 - Gerits, Sophie A1 - Goodall, Rhys A1 - Guha, Rishabh A1 - Hautier, Geoffroy A1 - Horton, Matthew A1 - Kaplan, Aaron A1 - Kingsbury, Ryan A1 - Kuner, Matthew A1 - Li, Bryant A1 - Linn, Xavier A1 - McDermott, Matthew A1 - Rohith Srinivaas Mohanakrishnan, A1 - Naik, Aakash A1 - Neaton, Jeffrey A1 - Persson, Kristin A1 - Petretto, Guido A1 - Purcell, Thomas A1 - Ricci, Francesco A1 - Rich, Benjamin A1 - Riebesell, Janosh A1 - Rignanese, Gian-Marco A1 - Rosen, Andrew A1 - Scheffler, Matthias A1 - Schmidt, Jonathan A1 - Shen, Jimmy-Xuan A1 - Sobolev, Andrei A1 - Sundararaman, Ravishankar A1 - Tezak, Cooper A1 - Trinquet, Victor A1 - Varley, Joel A1 - Vigil-Fowler, Derek A1 - Wang, Duo A1 - Waroquiers, David A1 - Wen, Mingjian A1 - Yang, Han A1 - Zheng, Hui A1 - Zheng, Jiongzhi A1 - Zhu, Zhuoying A1 - Jain, Anubhav T1 - Atomate2: Modular workflows for materials science N2 - High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2’s improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science. KW - Automation KW - DFT KW - Digitalisation KW - Materials design KW - Machine learning KW - Machine learned interatomic potentials PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624487 DO - https://doi.org/10.26434/chemrxiv-2025-tcr5h SN - 2573-2293 SP - 1 EP - 66 PB - American Chemical Society (ACS) CY - Washington, D.C. AN - OPUS4-62448 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ghata, Anupama A1 - Bernges, Tim A1 - Maus, Oliver A1 - Wankmiller, Björn A1 - Naik, Aakash A1 - Bustamante, Joana A1 - Gaultois, Michael W. A1 - Delaire, Olivier A1 - Hansen, Michael Ryan A1 - George, Janine A1 - Zeier, Wolfgang G. T1 - Exploring the Thermal and Ionic Transport of Cu+ Conducting Argyrodite Cu7PSe6 N2 - AbstractUnderstanding the origin of low thermal conductivities in ionic conductors is essential for improving their thermoelectric efficiency, although accompanying high ionic conduction may present challenges for maintaining thermoelectric device integrity. This study investigates the thermal and ionic transport in Cu7PSe6, aiming to elucidate their fundamental origins and correlation with the structural and dynamic properties. Through a comprehensive approach including various characterization techniques and computational analyses, it is demonstrated that the low thermal conductivity in Cu7PSe6 arises from structural complexity, variations in bond strengths, and high lattice anharmonicity, leading to pronounced diffuson transport of heat and fast ionic conduction. It is found that upon increasing the temperature, the ionic conductivity increases significantly in Cu7PSe6, whereas the thermal conductivity remains nearly constant, revealing no direct correlation between ionic and thermal transport. This absence of direct influence suggests innovative design strategies in thermoelectric applications to enhance stability by diminishing ionic conduction, while maintaining low thermal conductivity, thereby linking the domains of solid‐state ionics and thermoelectrics. Thus, this study attempts to clarify the fundamental principles governing thermal and ionic transport in Cu+‐superionic conductors, similar to recent findings in Ag+ argyrodites. KW - Thermoelectrics KW - Phonons KW - Chemically Complex Materials KW - DFT KW - Bonding Analysis PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-608866 DO - https://doi.org/10.1002/aenm.202402039 SP - 1 EP - 9 PB - Wiley AN - OPUS4-60886 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials N2 - An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features. KW - Bonding Analysis KW - DFT KW - High-throughput KW - Database KW - Phonons KW - Machine Learning PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-582892 DO - https://doi.org/10.1038/s41597-023-02477-5 VL - 10 IS - 1 SP - 1 EP - 18 AN - OPUS4-58289 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Naik, Aakash A1 - Ueltzen, Katharina A1 - Ertural, Christina A1 - Jackson, Adam J. A1 - George, Janine T1 - LobsterPy: A package to automatically analyze LOBSTERruns N2 - The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API. KW - Materials Science KW - Automation KW - Bonding Analysis KW - Materials Properties PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-595809 DO - https://doi.org/10.21105/joss.06286 VL - 9 IS - 94 SP - 1 EP - 4 PB - The Open Journal AN - OPUS4-59580 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine A1 - Petretto, G. A1 - Naik, Aakash A1 - Esters, M. A1 - Jackson, A. J. A1 - Nelson, R. A1 - Dronskowski, R. A1 - Rignanese, G.-M. A1 - Hautier, G. T1 - Automated bonding analysis with crystal orbital Hamilton populations (program code LobsterPy) N2 - This is the code for the program LobsterPy that can be used to automatically analyze and plot outputs of the program Lobster. KW - Automation KW - High-throughput computations KW - Bonding analysis PY - 2022 UR - https://doi.org/10.5281/zenodo.6320074 UR - https://doi.org/10.5281/zenodo.6415169 UR - https://doi.org/10.5281/zenodo.6415336 UR - https://doi.org/10.5281/zenodo.6581118 UR - https://doi.org/10.5281/zenodo.15034145 DO - https://doi.org/10.5281/zenodo.6320073 PB - Zenodo CY - Geneva AN - OPUS4-55174 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine A1 - Naik, Aakash A1 - Jackson, A. J. A1 - Baird, S. T1 - Scripts to reproduce "Automated bonding analysis with crystal orbital Hamilton populations" N2 - This repo allows to recreate our publication: https://doi.org/10.1002/cplu.202200123 In contrast to 0.2.2, we fixed an issue with absolute path. KW - Automation KW - High-throughput computations KW - Bonding analysis PY - 2022 UR - https://github.com/JaGeo/LobsterAutomation UR - https://doi.org/10.5281/zenodo.6421928 UR - https://doi.org/10.5281/zenodo.6595062 UR - https://doi.org/10.5281/zenodo.6599556 UR - https://doi.org/10.5281/zenodo.6674670 UR - https://doi.org/10.5281/zenodo.6704163 DO - https://doi.org/10.5281/zenodo.6421927 PB - Zenodo CY - Geneva AN - OPUS4-55177 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine A1 - Naik, Aakash T1 - DFT raw data for "Investigations on electron beam irradiated rare-earth doped SrF2 for application as low fading dosimeter material: Evidence for and DFT simulation of a radiation-induced phase" N2 - DFT raw data for the publication https://doi.org/10.1039/D2TC01773C It includes all predicted structures, the structure optimizations and the phonon computations in VASP format. Phonon computations were performed with the finite displacement method. KW - DFT KW - Phonons KW - Structure prediction PY - 2022 DO - https://doi.org/10.5281/zenodo.5942729 PB - Zenodo CY - Geneva AN - OPUS4-55417 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - George, Janine A1 - Petretto, G. A1 - Naik, Aakash A1 - Esters, M. A1 - Jackson, A. J. A1 - Nelson, R. A1 - Dronskowski, R. A1 - Rignanese, G.-M. A1 - Hautier, G. T1 - Cover profile for the article "Automated bonding analysis with crystal orbital Hamilton populuations" N2 - Invited for this month’s cover are researchers from Bundesanstalt für Materialforschung und -prüfung (Federal Institute for Materials Research and Testing) in Germany, Friedrich Schiller University Jena, Université catholique de Louvain, University of Oregon, Science & Technology Facilities Council, RWTH Aachen University, Hoffmann Institute of Advanced Materials, and Dartmouth College. The cover picture shows a workflow for automatic bonding analysis with Python tools (green python). The bonding analysis itself is performed with the program LOBSTER (red lobster). The starting point is a crystal structure, and the results are automatic assessments of the bonding situation based on Crystal Orbital Hamilton Populations (COHP), including automatic plots and text outputs. Coordination environments and charges are also assessed. More information can be found in the Research Article by J. George, G. Hautier, and co-workers. KW - Automation KW - Chemical bonds KW - High-throughput PY - 2022 DO - https://doi.org/10.1002/cplu.202200246 SN - 2192-6506 SP - 1 EP - 2 PB - Wiley-VCH CY - Weinheim AN - OPUS4-55557 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Naik, Aakash T1 - New descriptors for materials properties based on bonding indicators N2 - Includes a summary of the Ph.D. project that deals with generating a database populated with materials bonding properties and how we intend to gain deeper insights into material properties through this research. T2 - SALSA 2022 CY - Berlin, Germany DA - 15.09.2022 KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry PY - 2022 AN - OPUS4-56142 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Pauw, Brian Richard A1 - Laskina, Sofya A1 - Naik, Aakash A1 - Smales, Glen Jacob A1 - George, Janine A1 - Breßler, Ingo A1 - Benner, Philipp T1 - Jupyter notebook and VASP calculation details accompanying the manuscript: "Ultima Ratio: Simulating wide-range X-ray scattering and diffraction" N2 - Summary: This notebook and associated datasets (including VASP details) accompany a manuscript available on the ArXiv (https://doi.org/10.48550/arXiv.2303.13435) and hopefully soon in a journal as short communication as well. Most of the details needed to understand this notebook are explained in that paper with the same title as above. For convenience, the abstract is repeated here: Paper abstract: We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is presented coupled to the scattering intensity, may offer a novel pathway for determining the degree of crystallinity. The "Ultima Ratio" strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from $Q<0.01$\,$\mathrm{nm}^{-1}$ up to $Q\approx150$\,$\mathrm{nm}^{-1}$, with a resolution of 0.16\,\AA. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to $8000^3$ voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-$Q$ behaviour. The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder. While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts. KW - X-ray KW - Simulation KW - Scattering KW - MOUSE KW - Nanomaterials KW - XRD KW - SAXS KW - PDF KW - Total scattering KW - 3D Fourier transform KW - High resolution KW - FFT PY - 2023 DO - https://doi.org/10.5281/zenodo.7764044 PB - Zenodo CY - Geneva AN - OPUS4-57207 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 2) N2 - This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database. Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary. KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7821727 PB - Zenodo CY - Geneva AN - OPUS4-57440 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 1) N2 - This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database. Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary. KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7794811 PB - Zenodo CY - Geneva AN - OPUS4-57439 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 1) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) Refer to README.md file instructions to reproduce the data. KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7852082 PB - Zenodo CY - Geneva AN - OPUS4-57441 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 3) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7852791 PB - Zenodo CY - Geneva AN - OPUS4-57443 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 4) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7852798 PB - Zenodo CY - Geneva AN - OPUS4-57444 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 2) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7852107 PB - Zenodo CY - Geneva AN - OPUS4-57442 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Naik, Aakash T1 - Building quantum chemical orbital based bonding descriptor database N2 - Motivation, methodology and and results of our quantum chemical bonding descriptors database presented in form of a Poster T2 - RSC Twitter Conference 2023 CY - Online meeting DA - 28.02.2023 KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 UR - https://twitter.com/NaikAak/status/1630400167080869893 UR - https://twitter.com/NaikAak/status/1630540436434558977 AN - OPUS4-57101 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials N2 - Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information. T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 AN - OPUS4-57889 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 5) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7852806 PB - Zenodo CY - Geneva AN - OPUS4-57445 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 6) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7852808 PB - Zenodo CY - Geneva AN - OPUS4-57446 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -