TY - JOUR A1 - Gawek, Marcel A1 - Szymoniak, Paulina A1 - Hülagü, Deniz A1 - Hertwig, Andreas A1 - Schönhals, Andreas T1 - Molecular Mobility of Thin Films and the Adsorbed Layer of Poly(2-vinylpyridine) N2 - The molecular dynamics of thin films and the adsorbed layer of poly(2-vinylpyridine) (P2VP) were investigated using broadband dielectric spectroscopy (BDS) and spectroscopic ellipsometry. Thin films of P2VP were prepared on silicon substrates and characterized to understand the influence of film thickness on the thermal glass transition temperature (Tg) and molecular mobility. The ellipsometric study revealed a decrease in Tg with decreasing film thickness, attributed to the enhanced mobility at the polymer/air interface. The adsorbed layer, prepared via the solvent leaching approach, exhibited a higher Tg compared to the bulk, indicating reduced molecular mobility due to strong polymer substrate interactions. The dielectric measurements were carried out in two different electrode configurations, crossed electrode capacitors (CEC) and nanostructured electrodes (NSE), where the latter allows for a free surface layer at the polymer/air interface. The relaxation rates of the α-relaxation measured in the CEC geometry collapse into one chart independent from the film thickness. For the thin films measured in the NSE arrangement the relaxation rates slow down with decreasing film thickness which was discussed as related to a stronger interaction of the P2VP segments with the native SiO2 at the surface of the silicon substrate compared to aluminum. It is worth to note that the effect of the enhanced mobility at the polymer/air interface is not observed in the dielectric measurements. BDS measurements in NSE geometry identified an additional relaxation process (α*-relaxation) in thin films, which was more pronounced in the adsorbed layer. This process is hypothesized to be related to molecular fluctuations within the adsorbed layer including the adsorption/desorption dynamics of segments or to a Slow Arrhenius Process (SAP) related to the equilibration dynamics deep in the glassy state. KW - Thin polymer films PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-637181 DO - https://doi.org/10.1039/d5sm00539f SN - 1744-6848 SP - 1 EP - 12 PB - Royal Society of Chemistry (RSC) AN - OPUS4-63718 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schilling, Markus A1 - Marschall, Niklas A1 - Niebergall, Ute A1 - Böhning, Martin T1 - Modernizing FNCT data handling in polymer labs: Towards efficient management N2 - A streamlined Python-based workflow for transforming Full-Notch Creep Test (FNCT) data into organized, machine-actionable formats is presented. The workflow automates the conversion of raw FNCT data from classic CSV and Excel files into structured outputs that facilitate future semantic integration. Emphasizing practical data handling, the approach includes automation scripts for efficient data extraction, transformation, and storage, which culminate in well-ordered files. This transformation paves the way for potential semantic data integration and facilitates access for users with varying levels of digital experience to enhanced data management in polymer research and testing. KW - PE-HD KW - Full-Notch Creep Test (FNCT) KW - Environmental Stress Cracking (ESC) KW - Data Interoperability KW - FAIR Data KW - Semantic Data PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-637826 DO - https://doi.org/10.1016/j.commatsci.2025.114085 SN - 0927-0256 VL - 259 SP - 1 EP - 5 PB - Elsevier B.V. AN - OPUS4-63782 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Ueltzen, Katharina A1 - Naik, Aakash A. A1 - Ertural, Christina A1 - Benner, Philipp A1 - George, Janine T1 - Can simple exchange heuristics guide us in predicting magnetic properties of solids? N2 - A popular heuristic derived from the Kanamori-Goodenough-Anderson rules of superexchange connects bond angles and magnetism in certain transition metal compounds. We evaluate the fulfillment of this heuristic on databases of magnetic structures. The heuristic is partly satisfied in magnetic structures from experiment, and exceptions can be rationalized. We further show that incorporating this heuristic into machine learning models for magnetism improves the prediction. KW - Magnetism KW - Transition Metal Compounds KW - Materials Design KW - Sustainable Materials Design KW - Machine Learning KW - Materials Understanding PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-639290 DO - https://doi.org/10.26434/chemrxiv-2025-xj84d VL - Version 1 SP - 1 EP - 43 AN - OPUS4-63929 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ganose, Alex M. 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 C. A1 - Clary, Jacob A1 - Cohen, Orion A. A1 - Ertural, Christina A1 - Gallant, Max C. A1 - George, Janine A1 - Gerits, Sophie A1 - Goodall, Rhys E. A. A1 - Guha, Rishabh D. A1 - Hautier, Geoffroy A1 - Horton, Matthew A1 - Inizan, T. J. A1 - Kaplan, Aaron D. A1 - Kingsbury, Ryan S. A1 - Kuner, Matthew C. A1 - Li, Bryant A1 - Linn, Xavier A1 - McDermott, Matthew J. A1 - Mohanakrishnan, Rohith Srinivaas A1 - Naik, Aakash A. A1 - Neaton, Jeffrey B. A1 - Parmar, Shehan M. A1 - Persson, Kristin A. A1 - Petretto, Guido A1 - Purcell, Thomas A. R. A1 - Ricci, Francesco A1 - Rich, Benjamin A1 - Riebesell, Janosh A1 - Rignanese, Gian-Marco A1 - Rosen, Andrew S. 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 B. 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 - Materials design KW - DFT workflows KW - Phonons KW - Thermal conductivity KW - Bonding analysis PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-635759 DO - https://doi.org/10.1039/d5dd00019j SN - 2635-098X SP - 1 EP - 30 PB - Royal Society of Chemistry (RSC) CY - Cambridge AN - OPUS4-63575 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -