TY - CONF A1 - Waske, Anja T1 - X-ray Non-destructive testing of materials and composites N2 - Functional materials for energy conversion are important technology drivers needed for the implementation of low carbon energy. Therefore, researchers commonly focus on improving the intrinsic properties of a functional material. However, for applications, the extrinsic properties are at least as important as the intrinsic ones. Consequently, it is important to investigate and understand the external and internal structure of semi-finished products and especially defect dependent properties. The extrinsic properties may change during application and the life cycle of the material as well as through processing and molding steps. Our studies show how X-ray tomographic (XCT) investigations can contribute to structure investigations in composites and massive samples using the example of magnetic materials for energy conversion. T2 - Ruhr Universität Bochum - Seminar materials science and technology CY - Online meeting DA - 12.11.2020 KW - X-ray imaging KW - Non-destructuve testing KW - Functional materials PY - 2020 AN - OPUS4-51905 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stawski, Tomasz T1 - The structure of amorphous calcium sulfate and its role in the nucleation pathway and final mesostructure of CaSO 4 phases N2 - In recent years, we have come to appreciate the astounding intricacy of the formation process of minerals from ions in aqueous solutions. In this context, a number of studies have already revealed that nucleation in the CaSO4-H2O system is non-classical, where the formation of the different crystalline phases involves several steps including a common amorphous precursor. In this contribution a holistic view of the formation mechanism of gypsum and bassanite from solution will be presented. In short, our in situ and time-resolved scattering data demonstrate that calcium sulfate precipitation starts with the formation and aggregation of well-defined sub-3 nm primary species. These species constitute building “bricks'' of an amorphous precursor phase. We characterised the “bricks” by combining information obtained at different length-scales accessible at the mesoscale (from small-angle scattering) and at the atomic-length-scale (wide-angle scattering and high-energy diffraction). From these scattering data we derived pair distribution functions of the clusters and restricted their external shapes and dimensions. This allowed us to propose a structure of the primary species and to explore their dynamic properties with unbiased MD simulations using polarizable force fields. The formation of the amorphous phase involves the aggregation of these small primary species into larger disordered aggregates exhibiting “brick-in-the-wall” structure. The actual crystallisation occurs by the restructuring and coalescence of the “bricks” into a given calcium sulfate phase depending on the thermodynamic conditions of the solution. Importantly, these rearrangement processes by no means continue until a (nearly-)perfect homogeneous single crystal is obtained. Instead they come to a stop or at least significantly slow down. Such a process thus yields a final imperfect mesocrystal, composed of smaller domains rather than a continuous crystal structure, within which the domains are separated by an amorphous calcium sulfate phase. T2 - Goldschmidt Virtual 2021 CY - Online meeting DA - 04.07.2021 KW - Amorphous calcium sulfate KW - Scattering KW - SAXS/WAXS PY - 2021 UR - https://2021.goldschmidt.info/goldschmidt/2021/meetingapp.cgi/Paper/3847 AN - OPUS4-53621 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Battig, Alexander A1 - Müller, Patrick A1 - Bertin, Annabelle A1 - Schartel, Bernhard T1 - Hyperbranched Rigid Aromatic Phosphorus-Containing Flame Retardants for Epoxy Resins N2 - A rigid aromatic phosphorus-containing hyperbranched flame retardant structure is synthesized from 10-(2,5 dihydroxyphenyl)-10H-9-oxa- 10-phosphaphenanthrene-10-oxide (DOPO-HQ), tris(4-hydroxyphenyl)phosphine oxide (THPPO), and 1,4-terephthaloyl chloride (TPC). The resulting poly-(DOPO-HQ/THPPO-terephthalate) (PDTT) is implemented as a flame retardant into an epoxy resin (EP) at a 10 wt% loading. The effects on EP are compared with those of the monomer DOPO-HQ and triphenylphosphine oxide (OPPh3) as low molar mass flame retardants. The glass transition temperature, thermal decomposition, flammability (reaction to small flame), and burning behavior of the thermosets are investigated using differential scanning calorimetry, thermogravimetric analysis, pyrolysis combustion flow calorimetry, UL 94-burning chamber testing, and cone calorimeter measurements. Although P-contents are low at only 0.6 wt%, the study aims not at attaining V-0, but at presenting a proof of principle: Epoxy resinswith PDTT show promising fire performance, exhibiting a 25% reduction in total heat evolved (THE), a 30% reduction in peak heat release rate (PHRR) due to flame inhibition (21% reduction in effective heat of combustion (EHC)), and an increase in Tg at the same time. This study indicates that rigid aromatic hyperbranched polymeric structures offer a promising route toward multifunctional flame retardancy. KW - Hyperbranched KW - Aromatic KW - Phosphorus KW - Phosphine oxide KW - DOPO KW - Flame retardant KW - Xpoxy resin KW - Rigid PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-525910 DO - https://doi.org/10.1002/mame.202000731 SN - 1439-2054 VL - 306 IS - 4 SP - 731 PB - Wiley AN - OPUS4-52591 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine T1 - JaGeo/Advanced_Jobflow_Tutorial: v.0.1.0 N2 - This tutorial is aimed at developers who would like to develop workflows with Jobflow. This could include contributions to atomate2 and quacc. Jobflow workflows can also be executed with Fireworks on Supercomputers. This tutorial includes information on how to write a job for jobflows, how to connect jobs to a workflow including dynamic features and how to store job results in databases. The structure of the workflow is inspired by workflows that have been developed for atomate2 and quacc. This tutorial is also connected to google collab so that you can execute the code via their services. Please access the tutorial here: https://jageo.github.io/Advanced_Jobflow_Tutorial/intro.html KW - Automation KW - Computational Materials Science PY - 2023 UR - https://jageo.github.io/Advanced_Jobflow_Tutorial/intro.html DO - https://doi.org/10.5281/zenodo.8004401 PB - Zenodo CY - Geneva AN - OPUS4-57588 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 8) 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.7852823 PB - Zenodo CY - Geneva AN - OPUS4-57448 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ertural, Christina T1 - Automation of machine learning driven interatomic potential generation for predicting vibrational properties N2 - Investigating the phononic properties is beneficial for predicting low thermal conductivity thermoelectric materials.1–3 Employing density functional theory4 takes many calculation steps and consumes a lot of computational resources.5,6 Using machine learning driven interatomic potentials (MLIP, e.g., Gaussian approximation potential,8 GAP) opens up a faster route to phonons7 but in most cases, the potentials are specifically tailored for a certain compound. In this work, we automate the generation of such MLIPs in a Python code-based workflow, based on the automation tools atomate29 and pymatgen10 which combines the automatic DFT computations with the automated fitting of GAPs. Automation enables easier testing, benchmarking and validation.11 We aim to provide the workflow-generated potentials for storage in databases. T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Thermoelectrics PY - 2023 AN - OPUS4-57932 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ertural, Christina T1 - Quantum chemical bonding analysis of functional materials using LOBSTER and automated workflows N2 - A talk on how to use LOBSTER and automated workflows (supported by LobsterPy, atomate2 and more) to do quantum chemical bonding analysis for (functional) materials design. T2 - Gastvortrag Inorganic Chemistry Laboratory, University of Oxford CY - Oxford, UK DA - 04.05.2023 KW - Chemical bonding KW - Automated workflows KW - Functional materials PY - 2023 AN - OPUS4-57933 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ertural, Christina T1 - Workflows and automation (lecture/tutorial) N2 - A lecture and tutorial on how to use workflows and automation methods for quantum chemical calculations. T2 - Invited lecture CY - Oxford, UK DA - 28.04.2023 KW - Workflows KW - Automation KW - Quantum chemical bonding analysis PY - 2023 AN - OPUS4-57934 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bustamante, Joana A1 - Naik, Aakash A1 - Ertural, Christina A1 - Ueltzen, Katharina A1 - George, Janine T1 - Thermodynamic and Thermoelectric Properties of the Canfieldite, (Ag8SnS6 ), in the Quasi-Harmonic Approximation N2 - Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties.One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, a new low-temperature (LT) phase transition of canfieldite at 120K has been found. Here, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Thermal properties such as the constant-pressure heat capacity (Cp) and thermal conductivity are very close to experimental measurements. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with argyrodites analogues, Ag8XS6 (X = Sn, Si, Ge), to arrive at an improved T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - Thermoelectric materials KW - DFT KW - QHA KW - Grüneisen parameter PY - 2023 AN - OPUS4-57887 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Thekkeppat, N. P. A1 - Bhattacharya, Biswajit A1 - Tothadi, S. A1 - Ghosh, S. T1 - Mechanically flexible crystals of styryl quinoline derivatives N2 - Herein, we report three crystals of styryl quinoline derivatives. All these three crystals are mechanically flexible crystals, isostructural and also all of them comply with the common underlying features for elas- tic flexibility like absence of slip plane, criss-cross packing arrangement of neighbouring tapes, presence of weak and dispersive interactions such as halogen bonds, hydrogen bonds etc. The interactions facilitate easy movement of molecules under application of pressure thereby imparting elasticity. Further, the crys- tals were found to be blue light emitting making them promising candidates for optical waveguides. The optical properties were combined with flexibility by using crystal engineering approach towards achiev- ing various applications such as flexible OLEDs, optical waveguides, flexible optoelectronics etc. KW - Mechanical flexibility KW - Halogen bond KW - Fluorescence PY - 2022 DO - https://doi.org/10.1016/j.molstruc.2022.133293 SN - 0022-2860 VL - 1265 SP - 1 EP - 7 PB - Elsevier CY - Amsterdam AN - OPUS4-55549 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Amariamir, Sasan A1 - Benner, Philipp A1 - George, Janine T1 - Prediction of materials synthesizability N2 - In the pursuit of discovering materials with desirable properties, extending the available material libraries is crucial. High-throughput simulations have become an integral part in designing new materials in the past decades. However, there is no straightforward way of distinguishing synthesizable materials from all the proposed candidates. This project focuses on employing AI-driven methods to estimate synthesizability of materials. Up to now, material scientists and engineers have relied on domain knowledge as well as empirical heuristics to guess the stability and synthesizability of molecules and crystals. The famous Pauling rules of crystal stability are an example of such heuristics. However, after the accelerating material discovery in all the years since Pauling, these rules now fail to account for the stability of most known crystals. A new predictive set of heuristics for crystal stability/synthesizability is unlikely to be uncovered by human perception, given the magnitude and dimensionality of crystallographic data. Hence, a data-driven approach should be proposed to find a predictive model or set of heuristics which differentiate synthesizable crystal structures from the rest. The main challenge of this research problem is the lack of a negative set for classification. Here, there are two classes of data: the positive class which contains synthesizable materials and the negative class which contains materials which are not synthesizable. While the data from the positive class is simply the data of crystals which have been experimentally synthesized, we do not have access to data points which are certainly unsynthesizable. Strictly speaking, if an attempt of synthesizing a crystal fails, it does not necessarily follow that the crystal is not synthesizable. Also, there is no database available which contains the intended crystal structures of unsuccessful synthesis attempts. This project proposes a semi-supervised learning scheme to predict crystal synthesizability. The ML model is trained on experimental and theoretical crystal data. The initial featurization focuses on local environments which is inspired by the Pauling Rules. The experimental data points are downloaded through the Pymatgen API from the Materials Project database which contains relaxed structures recorded in Inorganic Crystal Structure Database – ICSD. The theoretical data is queried from select databases accessible through the Optimade project’s API. T2 - MSE Congress 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Synthesizability KW - PU Learning KW - Cheminformatics PY - 2022 AN - OPUS4-56731 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Witt, Julia T1 - Exploring innovative Materials and in situ techniques N2 - Presentation of some projects conducted in recent years related to corrosion studies and research on electrocatalysis. The talk was part of a seminar at the University of Toronto and took place within the framework of a scientific exchange stay. T2 - Seminar at the University of Toronto CY - Toronto, Canada DA - 11.10.2023 KW - Multi-principal element alloys (MPEAs) KW - Electrocatalysis KW - Corrosion KW - Materials acceleration platforms (MAPs) PY - 2023 AN - OPUS4-59029 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waske, Anja A1 - Fähler, Sebastian T1 - Thermomagnetic generators with magnetocaloric materials for harvesting low grade waste heat N2 - To date, there are very few technologies available for the conversion of low-temperature waste heat into electricity. Thermomagnetic generators are one approach proposed more than a century ago. Such devices are based on a cyclic change of magnetization with temperature. This switches a magnetic flux and, according to Faraday’s law, induces a voltage. Here we give an overview on our research, covering both materials and systems. We demonstrate that guiding the magnetic flux with an appropriate topology of the magnetic circuit improves the performance of thermomagnetic generators by orders of magnitude. Through a combination of experiments and simulations, we show that a pretzel-like topology results in a sign reversal of the magnetic flux. This avoids the drawbacks of previous designs, namely, magnetic stray fields, hysteresis and complex geometries of the thermomagnetic material. Though magnetocaloric materials had been the first choice also for thermomagnetic generators, they require some different properties, which we illustrate with Ashby plots for materials selection. Experimentally we compare La-Fe-Co-Si and Gd plates in the same thermomagnetic generator. Furthermore, we discuss corrosion and deterioration under cyclic use is a severe problem occurring during operation. To amend this, composite plates using polymer as a matrix have been suggested previously. T2 - Dresden Days of Magnetocalorics CY - Dresden, Germany DA - 13.11.2023 KW - Thermomagnetic material KW - Waste heat recovery KW - Generator PY - 2023 AN - OPUS4-58865 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waske, Anja T1 - Ethics in research for PhD students N2 - The talk gives a short overview and explains basic principles of research ethics for PhD students. We examine the most important principles in the codes of conduct for good scientific practice on EU and national level. Based on these general rules, we give some hands-on practical tips for daily scientific work and discuss in detail one of the fields most susceptible to conflict, the topic of authorship in scientific publications. T2 - Kick-off meeting ITN USES2 CY - Online meeting DA - 09.11.2023 KW - Research ethics KW - PhD training PY - 2023 AN - OPUS4-58783 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gupta, Kanhaiya T1 - Classification of pores from micro-XCT data of additively manufactured components prepared by laser powder bed fusion. N2 - Laser powder bed fusion (PBF-LB/M) is a metal-based additive manufacturing (AM) process that utilizes a laser to melt powder particles selectively and form the desired product through a layer-by-layer fabrication process. It allows access to a vast new design space and can produce parts with far greater geometrical complexity than traditional methods. However, PBF-LB/M induces inevitable microstructural defects, such as voids and pores, that can adversely affect the quality and performance of the manufactured components. Hence, knowledge of pore formation, types, and suppression is essential for successful future AM applications. This research investigates the formation of different types of pores and their quantification from X-ray micro-computed tomography images of a formerly reported sample. It also examines the influence of the volumetric energy density (VED) on the size, shape, and location of pores for 316L stainless steel parts produced by the PBF-LB/M process. T2 - FEMS EUROMAT 2023 CY - Frankfurt a. M., Germany DA - 03.09.2023 KW - Additive manufacturing KW - Laser Powder Bed Fusion KW - X-ray Computed Tomography PY - 2023 AN - OPUS4-58337 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Sergievskaya, A. A1 - Absil, R. A1 - Chauvin, A. A1 - Yusenko, Kirill A1 - Vesely, J. A1 - Godfroid, T. A1 - Konsstantinidis, S. T1 - Sputtering onto liquids: How does the liquid viscosity affect the formation of nanoparticles and metal films? N2 - This paper reports on the effect of the solvent viscosity on the formation of gold nanoparticles (Au NPs) during Sputtering onto Liquid (SoL) process. All other parameters related to the plasma and the host liquid are kept constant. SoL is a simple highly reproducible approach for preparation of colloidal dispersions of small naked NPs. The properties of the final product are determined by both the sputtering parameters and the host liquid characteristics. As a model system we chose to sputter a gold target by a direct-current magnetron discharge onto a line of polymerized rapeseed oils having similar surface tension (32.6 ― 33.1 mJ·m-2 at RT). It was found that well dispersed Au NPs grow in the bulk solution of oils with low viscosities (below 630 cP at 25 °C) while gold films form onto the surface of high viscosity liquids (more than 1000 cP at 25 °C). The mean diameter of the individual Au NPs slightly increases with oil viscosity and is in range about 2.1―2.5 nm according to transmission electron microscopy. KW - Liquid spattering KW - Nanoparticles PY - 2023 DO - https://doi.org/10.1039/D2CP03038A SN - 1463-9084 VL - 25 IS - 4 SP - 2803 EP - 2809 PB - RSC Publ. CY - Cambridge AN - OPUS4-56562 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Alić, Jasna A1 - Schlegel, Moritz-Caspar A1 - Emmerling, Franziska A1 - Stolar, Tomislav T1 - Meeting the UN Sustainable Development Goals with Mechanochemistry N2 - Chemistry traditionally relies on reactions in solution, but this method is increasingly problematic due to the scale of chemical processes and their economic and environmental impact. Handling residual chemical waste, including solvents, incurs significant costs and environmental pressure. Conversely, novel chemical approaches are needed to address pressing societal issues such as climate change, energy scarcity, food insecurity, and waste pollution. Mechanochemistry, a sustainable chemistry discipline that uses mechanical action to induce chemical reactivity without bulk solvents, is a hot topic in academic research on sustainable and green chemistry. Given its fundamentally different working principles from solution chemistry, mechanochemistry offers more efficient chemical processes and the opportunity to design new chemical reactions. Mechanochemistry has a profound impact on many urgent issues facing our society and it is now necessary to use mechanochemistry to address them. This Minireview aims to provide a guide for using mechanochemistry to meet the United Nations (UN) Sustainable Development Goals (SDGs), thereby contributing to a prosperous society. Detailed analysis shows that mechanochemistry connects with most UN SDGs and offers more cost‐efficiency than other approaches together with a superior environmental performance. KW - Mechanochemistry KW - SDGs PY - 2024 DO - https://doi.org/10.1002/anie.202414745 SP - 1 EP - 3 PB - Wiley AN - OPUS4-61108 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ertural, Christina T1 - Phonon-accurate machine-learning potentials from automated workflows N2 - Data-driven materials design aims to predict and optimise material properties, such as stability and thermal conductivity, that are influenced by vibrational behaviour. Approaches such as DFT are computationally demanding and have limitations for phonon calculations. Machine learning based interatomic potentials (MLIP), such as the Gaussian Approximation Potential (GAP), offer a more efficient alternative. We have developed a Python workflow to automate MLIP generation using the Materials Project database. DFT calculations, MLIP fitting and benchmarking steps are automated. This approach speeds up phonon calculations and allows testing of different data generation strategies and hyperparameters. Our goal is to provide open source and share these capabilities to improve reproducibility and accessibility in computational chemistry. This talk will provide a hih-level overview of the automated software and recent preliminary results. T2 - Berlin PostDoc Day 2024 CY - Berlin, Germany DA - 07.11.2024 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Thermoelectrics KW - Automated workflows PY - 2024 AN - OPUS4-61576 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Nag, Sayak A1 - Emmerling, Franziska A1 - Tothadi, Srinu A1 - Bhattacharya, Biswajit A1 - Ghosh, Soumyajit T1 - Distinct photomechanical responses of two new 1,3-dimethylbarbituric acid derivative crystals N2 - We demonstrate two distinct photomechanical responses (i.e. photomechanical bending and photosalient bursting) of two new 1,3-dimethylbarbituric acid derivative crystals based on tailoring their substituents and the modulation of their spacers. KW - Crystal engineering KW - Fexible crystals PY - 2024 DO - https://doi.org/10.1039/D4CE00233D SP - 1 EP - 12 PB - Royal Society of Chemistry (RSC) AN - OPUS4-60141 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waske, Anja T1 - Fundamentals of Energy harvesting N2 - An introduction to the principles of energy harvesting and conversion, the main technology types and their application is given. A particular focus is on thermomagnetic energy harvesting. T2 - EU ITN Heat4Energy School CY - Dresden DA - 14.10.2024 KW - Energy harvesting KW - Magnetic materials PY - 2024 AN - OPUS4-62285 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Li, Yue A1 - Colnaghi, Timoteo A1 - Gong, Yilun A1 - Zhang, Huaide A1 - Yu, Yuan A1 - Wei, Ye A1 - Gan, Bin A1 - Song, Min A1 - Marek, Andreas A1 - Rampp, Markus A1 - Zhang, Siyuan A1 - Pei, Zongrui A1 - Wuttig, Matthias A1 - Ghosh, Sheuly A1 - Körmann, Fritz A1 - Neugebauer, Jörg A1 - Wang, Zhangwei A1 - Gault, Baptiste T1 - Machine learning‐enabled tomographic imaging of chemical short‐range atomic ordering N2 - In solids, chemical short‐range order (CSRO) refers to the self‐organization of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, it is showcased how machine learning‐enhanced atom probe tomography (APT) can mine the near‐atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium‐entropy alloy. Multiple CSRO configurations are revealed, with their formation supported by state‐of‐the‐art Monte‐Carlo simulations. Quantitative analysis of these CSROs allows establishing relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic‐scale architectures. KW - Chemical short-range order (CSRO) KW - Atom probe tomography (APT) KW - Machine learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623777 DO - https://doi.org/10.1002/adma.202407564 SN - 1521-4095 VL - 36 IS - 44 SP - 1 EP - 9 PB - Wiley-VCH CY - Weinheim AN - OPUS4-62377 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Dolai, Malay A1 - Saha, Urmila A1 - Goswami, Juli Nanda A1 - Bhattacharya, Biswajit A1 - Emmerling, Franziska A1 - Ifseisi, Ahmad A. A1 - Biswas, Surajit ED - Bhattacharya, Biswajit T1 - Nickel(II)-mediated in situ complex formation with unexpected ligand transformations: crystal structures, DFT calculations, and catalytic activity in CO2 fixation reactions N2 - Two transformed ligands, namely 1,3,5-triazapentadienato(imidoylamidinato) and N-(methoxy(pyrimidin-2-yl)(pyrimidine-2-carboxamido)methyl)pyrimidine-2-carboxamide, are reported here as the first products derived from 2-cyano pyridine/pyrimidine, respectively, under nickel metal mediation in the presence of hydroxylamine hydrochloride in situ. Two different nickel-coordinated complexes (1 and 2) were formed. Single crystal X-ray diffraction revealed that complex 1 is a mononuclear complex with tetra-coordinated square planar geometry, whereas complex 2 is a dinuclear complex with hexa-coordinated distorted octahedral geometry. The synthetic pathways of both transformed ligands are explained with the support of energy calculations of different states via density functional theory (DFT) calculations. The catalytic addition reactions of epoxides and carbon dioxide efficiently produce organic cyclic carbonates in the presence of complexes 1–2, attributed to the presence of Lewis acidic nickel and Lewis basic centers, such as free amine, imine, methoxy, or nitrogen in complexes 1 and 2. Under mild reaction conditions (1 atm CO2 pressure and 60 °C temperature), different types of epoxides (exocyclic, endocyclic, and aromatic epoxides) yield high levels of respective cyclic carbonates in the presence of both complexes 1–2 as catalysts, with tert-butylammonium bromide (TBAB) acting as a co-catalyst. The high turnover number (TON) and turnover frequency (TOF) of these catalytic reactions clearly indicate the efficacy of the two catalysts. KW - Catalytic activity in CO2 fixation reactions DFT calculations KW - Nickel(II)-complex KW - In situ complex formation KW - Crystal Structure PY - 2025 DO - https://doi.org/10.1039/d4nj01801j SN - 1369-9261 VL - 49 IS - 11 SP - 4451 EP - 4463 PB - Royal Society of Chemistry (RSC) AN - OPUS4-62782 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schneider, Mike A1 - Couzinié, Jean-Philippe A1 - Shalabi, Amin A1 - Ibrahimkhel, Farhad A1 - Ferrari, Alberto A1 - Körmann, Fritz A1 - Laplanche, Guillaume T1 - Effect of stacking fault energy on the thickness and density of annealing twins in recrystallized FCC medium and high-entropy alloys N2 - This work aims to predict the microstructure of recrystallized medium and high-entropy alloys (MEAs and HEAs) with a face-centered cubic structure, in particular the density of annealing twins and their thickness. Eight MEAs and five HEAs from the Cr-Mn-Fe-Co-Ni system are considered, which have been cast, homogenized, cold-worked and recrystallized to obtain different grain sizes. This work thus provides a database that could be used for data mining to take twin boundary engineering for alloy development to the next level. Since the stacking fault energy is known to strongly affect recrystallized microstructures, the latter was determined at 293 K using the weak beam dark-field technique and compared with ab initio simulations, which additionally allowed to calculate its temperature dependence. Finally, we show that all these data can be rationalized based on theories and empirical relationships that were proposed for pure metals and binary Cu-based alloys. KW - CoCrFeMnNi HEAs and MEAs KW - Recrystallization KW - Transmission electron microscopy (TEM) KW - Electron backscatter diffraction KW - Grain boundary engineering PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-629346 DO - https://doi.org/10.1016/j.scriptamat.2023.115844 SN - 1359-6462 VL - 240 SP - 1 EP - 6 PB - Elsevier CY - Amsterdam AN - OPUS4-62934 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Omar, Hassan A1 - Ahmadi, Shayan A1 - Szymoniak, Paulina A1 - Schönhals, Andreas T1 - Molecular Mobility of Thin Films of Poly (bisphenol-A carbonate) Capped and with one Free Surface: From Bulk-like Samples down to the Adsorbed Layer N2 - The molecular mobility of thin films of poly(bisphenol A carbonate) (PBAC) was systematically investigated using broadband dielectric spectroscopy, employing two distinct electrode configurations. First, films were prepared in a capped geometry between aluminum electrodes employing a crossed electrode capacitor (CEC) configuration, down to film thicknesses of 40 nm. The Vogel temperature, derived from the temperature dependence of relaxation rate of the α-relaxation, increases with decreasing film thickness characterized by an onset thickness. The onset thickness depends on the annealing conditions, with less intense annealing yielding a lower onset thickness. Additionally, a broadening of the β-relaxation peak was observed with decreasing thickness, attributed to the interaction of phenyl groups with thermally evaporated aluminum, resulting in a shift of certain relaxation modes to higher temperatures relative to the bulk material. A novel phenomenon, termed the slow Arrhenius process (SAP), was also identified in proximity to the α-relaxation temperature. For films with thicknesses below 40 nm, nanostructured electrodes (NSE) were utilized, incorporating nanostructured silica spacers to establish a free surface with air. This free surface causes an enhancement in the molecular mobility for the 40 nm sample, preserving the β-relaxation as a distinct peak. The α-relaxation was detectable in the dielectric loss down to 18 nm, shifting to higher temperatures as film thickness is decreased. Notably, the onset thickness for the increase in Vogel temperature was lower in the NSE configuration compared to the CEC setup, attributed to the presence of the polymer-air interface. KW - Thin polymer films PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623422 DO - https://doi.org/10.1039/d4sm01238k SN - 1744-6848 VL - 21 IS - 2 SP - 241 EP - 254 PB - RSC AN - OPUS4-62342 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Yusenko, Kirill T1 - Reaction of high-entropy alloys with hydrogen under extreme conditions N2 - In the current study, we investigate an interaction under high-pressure high-temperature of single phase fcc-, hcp- and bcc-structured high-entropy alloys with hydrogen, carbon and nitrogen to obtain high-entropy hydrides, carbides and nitrides. Structural changes in high-entropy alloys upon compression and heating in the presence of these light elements are in the focus of our investigation. An easy route to high-entropy hydrides, carbides and nitrides will open new synthetic horizons in compositionally complex materials. Our study suggests that high-entropy alloys form high- entropy hydrides mainly with a composition close to M:H 1:1 ratio. Hydrides can be obtained under compression with hydrogen as a pressure compression medium or using hydrogen fluid as reactive agent. T2 - Intermetallics 2023 CY - Bad Staffelstein, Germany DA - 03.10.2023 KW - HEA PY - 2023 AN - OPUS4-58555 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kimiaei, E. A1 - Farooq, M. A1 - Szymoniak, Paulina A1 - Ahmadi, Shayan A1 - Babaeipour, S. A1 - Schönhals, Andreas A1 - Österberg, M. T1 - The role of lignin as interfacial compatibilizer in designing lignocellulosic-polyester composite films N2 - Advancing nanocomposites requires a deep understanding and careful design of nanoscale interfaces, as interfacial interactions and adhesion significantly influence the physical and mechanical properties of these materials. This study demonstrates the effectiveness of lignin nanoparticles (LNPs) as interfacial compatibilizer between hydrophilic cellulose nanofibrils (CNF) and a hydrophobic polyester, polycaprolactone (PCL). In this context, we conducted a detailed analysis of surface-to-bulk interactions in both wet and dry conditions using advanced techniques such as quartz crystal microbalance with dissipation (QCM-D), atomic force microscopy (AFM), water contact angle (WCA) measurements, broadband dielectric spectroscopy (BDS), and inverse gas chromatography (IGC). QCM-D was employed to quantify the adsorption behavior of LNPs on CNF and PCL surfaces, demonstrating LNPs’ capability to interact with both hydrophilic and hydrophobic phases, thereby enhancing composite material properties. LNPs showed extensive adsorption on a CNF model film (1186 ± 178 ng.cm−2) and a lower but still significant adsorption on a PCL model film (270 ± 64 ng.cm−2). In contrast, CNF adsorption on a PCL model film was the lowest, with a sensed mass of only 136 ± 35 ng.cm−2. These findings were further supported by comparing the morphology and wettability of the films before and after adsorption, using AFM and WCA analyses. Then, to gain insights into the molecular-level interactions and molecular mobility within the composite in dry state, BDS was employed. The BDS results showed that LNPs improved the dispersion of PCL within the CNF network. To further investigate the impact of LNPs on the composites’ interfacial properties, IGC was employed. This analysis showed that the composite films containing LNPs exhibited lower surface energy compared to those composed of only CNF and PCL. The presence of LNPs likely reduced the availability of surface hydroxyl groups, thus modifying the physicochemical properties of the interface. These changes were particularly evident in the heterogeneity of the surface energy profile, indicating that LNPs significantly altered the interfacial characteristics of the composite materials. Overall, these findings emphasize the necessity to control the interfaces between components for next-generation nanocomposite materials across diverse applications. KW - Lignin KW - Nanocomposites PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615098 DO - https://doi.org/10.1016/j.jcis.2024.10.083 SN - 0021-9797 VL - 679 SP - 263 EP - 275 PB - Elsevier Inc. AN - OPUS4-61509 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Xu, Q. A1 - Szymoniak, Paulina A1 - Kolmangadi, Mohamed A. A1 - Yang, Z. A1 - Wang, S. A1 - Gao, Y. A1 - Shang, J. A1 - Hunger, J. A1 - Aldiyarov, A. A1 - Schönhals, Andreas A1 - Ge, Y. A1 - Qi, Z. T1 - Molecular engineering of supramolecular polymer adhesive with confined water and a single crown ether N2 - Here, we report a water-induced supramolecular polymer built from confined structural water and an intrinsic amphiphilic macrocyclic self-assembly in a nanophase separated structure. The newly designed crown ether macrocycle, featuring strong hydrophilic hydrogen bonding receptor selenoxide and a synergistical hydrophobic selenium-substituted crown core, confines the structural water in a segregated, interdigitated architecture. Although water molecules typically freeze around 0 °C, the confined structural water in this supramolecular polymer remains in a liquid-like state down to 80 °C. Previous studies suggest that multiple crown ether units are needed to generate structural water. However, here, one unit is sufficient to control the formation and disappearance of structural water and consequent supramolecular polymerization. Typically, the DC conductivity of water shows Arrhenius temperature dependency (lnσDC ∝ 1/T). In contrast, this new crown unit maintains water in confined states, which exhibit a Vogel/Fulcher/Tammann behavior (lnσDC ∝ 1/(T-T0)) at temperatures above the glass transition temperature. Moreover, this water-induced supramolecular polymer exhibits remarkable adhesion properties to hydrophilic surfaces and maintains tough adhesion at low temperatures. These findings show how a single small macrocycle can govern the complex structure and functionality of water in supramolecular systems. KW - Supramolecular polymerization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623631 DO - https://doi.org/10.1039/D4SC06771A SN - 2041-6539 VL - 16 SP - 1 EP - 9 PB - RSC AN - OPUS4-62363 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Szymoniak, Paulina A1 - Kolmangadi, Mohamed A. A1 - Schönhals, Andreas T1 - Application of Fast Scanning Calorimetry in Soft Matter Research – Two Examples: 1. Polymers for Gas separation membranes 2. Ionic Liquid Crystals N2 - The application of fast scanning calorimetry (FSC) with heating rates in the range from 10 K/s to 10,000 K/s in soft matter research is discussed through two examples. In the first part, FSC is applied to polymers of intrinsic microporosity (PIMs). No glass transition could be measured for these polymers by conventional calorimetry before their degradation. By decoupling the time scales of chemical degradation and the glass transition, it could be shown for the first time that PIMs undergo a glass transition. In the second part, FSC is applied to ionic liquid crystals to investigate their molecular mobility. In order to cover a broad dynamical range, FSC is combined with temperature-modulated differential scanning calorimetry, and temperature-modulated FSC. T2 - Colloqium at the physics department of the Charles university Prague CY - Prague, Czech Republic DA - 12.11.2024 KW - Fast Scanning Calorimetry PY - 2024 AN - OPUS4-61670 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schoenhals, Andreas T1 - The bahvior of inonic liquid crystals as investigated by broadband dielectric spectroscopy and complementary methodas N2 - The behavior of liquid crystals based on phenylbenzoate was investigated using broadband dielectric spectroscopy and advanced calorimetry. The length of the alkyl side chain was systematically varied from 8 to 16 carbon atoms. The experimental results were compared with those obtained from molecular dynamics simulations. T2 - Sympoium: "Dipoles is soft matter - How they move and organize" CY - Leuven, Belgium DA - 06.06.2025 KW - Ionic liquid crystals PY - 2025 AN - OPUS4-63316 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Quosdorf, Heike T1 - A Digital Object Identifier for Additively Manufactured Parts as Open Source Software Package N2 - A method to uniquely identify samples without printed or handwritten labels is an advantage not just for additively manufactured parts. To kickstart industry use cases it is important to provide a ready made implementation kit. Following an open science and open source software approach Germanys Federal Institute for Materials Research and Testing BAM seeks to promote digital solutions of ongoing Research projects. With this software package a novel method based on microstructural features as identifiers DOI4AM (digital object identifier for additively manufactured parts will be explained alongside its implementation as open source Python software package. The digital object identifier (DOI) links product data clearly and forgery proof with real components. Its implementation helps to identify and securely authenticate additively manufactured components during its product life cycle by using characteristic microstructure features just like a fingerprint. To calculate the DOI fingerprint, a few preprocessing steps need to be performed to detect the uniquely distributed microstructure features that occur during the 3D printing process. A go through guide show s the preprocessing steps that include computer tomography (CT) image capturing, feature segmentation and data distribution via CSV files. While all steps can be followed along in a Jupyter notebook with sample data, the software package includes functions to create and compare fingerprints, as well, as an application programming interface (API) for integration in existing software platforms. A quick showcase of our industry partners implementation of the algorithm as containerized micro service in their digital product passport (DPP) web solution PASS X proves the first successful technology transfer of this project. T2 - MSE Research Data Forum 2025 CY - Siegburg, Germany DA - 08.07.2025 KW - Open Science KW - Authentication KW - Unique identification KW - Digital fingerprint KW - X-ray Computed Tomography KW - Additive manufacturing KW - Open Source Software PY - 2025 AN - OPUS4-63904 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Szymoniak, Paulina A1 - Kolmangadi, Mohamed A. A1 - Böhning, Martin A1 - De Souza, N. R. A1 - Juranyi, F. A1 - Zorn, R. A1 - Schönhals, Andreas T1 - Inelastic and Quasielastic Neutron Scattering on Polynorbornenes with Bulky Carbocyclic Side groups N2 - This study investigates the molecular mobility and vibrational properties of polynorbornenes with bulky carbocyclic side groups using inelastic and quasielastic neutron scattering techniques. The polymers, synthesized via metathesis and addition polymerization, exhibit varying degrees of microporosity, which ignificantly influences their gas separation performance. By inelastic neutron scattering experiments, it could be shown that all considered polymers have excess contributions to the low frequency vibrational density of states known as the Boson peak. The maximum frequency of the Boson peak correlates to the microporosity of the polymers. This correlation supports the sound wave interpretation of the Boson peak, suggesting that the microporous structure enhances the compressibility of the material at a microscopic length scale. The molecular mobility, particularly the methyl group rotation, was characterized using elastic scans and quasielastic neutron scattering. The study revealed a temperature dependent relaxation process, with the onset of molecular fluctuations observed around 200 K for the polymer containing methyl groups. For the polymer having no methyl groups only elastic scattering is observed. The methyl group rotation was analyzed in terms of a jump diffusion in a threefold potential with three equivalent energy minima. This leads to an almost correct description of the q dependence of the elastic incoherent scattering function when the number of hydrogen nuclei undergoing the methyl group rotation is considered. It was further evidenced that the fraction of methyl undergoing the methyl group rotation increases with increasing temperature. KW - Polymers of Intrinsic Microporosity KW - Neutron scattering PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630297 DO - https://doi.org/10.1016/j.polymer.2025.128358 SN - 1873-2291 VL - 328 SP - 241 PB - Elsevier Ltd. AN - OPUS4-63029 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bernges, T. A1 - Hanus, R. A1 - Wankmiller, B. A1 - Imasato, K. A1 - Lin, S. A1 - Ghidiu, M. A1 - Gerlitz, M. A1 - Peterlechner, M. A1 - Graham, S. A1 - Hautier, G. A1 - Pei, Y. A1 - Hansen, M. R. A1 - Wilde, G. A1 - Snyder, G. J. A1 - George, Janine A1 - Agne, M T. A1 - Zeier, W. G. T1 - Considering the Role of Ion Transport in Diffuson-Dominated Thermal Conductivity N2 - Next-generation thermal management requires the development of low lattice thermal conductivity materials, as observed in ionic conductors. For example, thermoelectric efficiency is increased when thermal conductivity is decreased. Detrimentally, high ionic conductivity leads to thermoelectric device degradation. Battery safety and design also require an understanding of thermal transport in ionic conductors. Ion mobility, structural complexity, and anharmonicity have been used to explain the thermal transport properties of ionic conductors. However, thermal and ionic transport are rarely discussed in direct comparison. Herein, the ionic conductivity of Ag+ argyrodites is found to change by orders of magnitude without altering the thermal conductivity. Thermal conductivity measurements and two-channel lattice dynamics modeling reveal that the majority of Ag+ vibrations have a non-propagating diffuson-like character, similar to amorphous materials. It is found that high ionic mobility is not a requirement for diffuson-mediated transport. Instead, the same bonding and structural traits that can lead to fast ionic conduction also lead to diffuson-mediated transport. Bridging the fields of solid-state ionics and thermal transport, it is proposed that a vibrational perspective can lead to new design strategies for functional ionic conducting materials. As a first step, the authors relate the so-called Meyer-Neldel behavior in ionic conductors to phonon occupations. KW - Diffusons KW - DFT KW - Phonons KW - Thermoelectrics PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547140 DO - https://doi.org/10.1002/aenm.202200717 SN - 1614-6832 VL - 12 IS - 22 SP - 1 EP - 13 PB - Wiley VHC-Verlag AN - OPUS4-54714 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 - Automated bonding analysis with crystal orbital Hamilton populuations N2 - Automated bonding analysis software has been developed based on Crystal Orbital Hamilton Populations to facilitate high-throughput bonding analysis and machine-learning of bonding features. This work presents the software and discusses its applications to simple and complex materials such as GaN, NaCl, the oxynitrides XTaO2N (X=Ca, Ba, Sr) and Yb14Mn1Sb11. KW - Chemical bonds KW - Automation KW - High-throughput PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-551641 DO - https://doi.org/10.1002/cplu.202200123 SN - 2192-6506 SP - 1 EP - 11 PB - Wiley-VCH CY - Weinheim AN - OPUS4-55164 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Arnold, M. A1 - Katzmann, J. A1 - Naik, Aakash A1 - Görne, A. L. A1 - Härtling, Thomas A1 - George, Janine A1 - Schuster, C. T1 - 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 - A recent approach to measure electron radiation doses in the kGy range is the use of phosphors with an irradiation dose-dependent luminescence decay time. However, the applicability of the previously investigated material NaYF4:Yb3+,Er3+ is limited as it shows pronounced fading. Therefore, in this work, a modified SrF2 synthesis is presented that results in SrF2 nanoparticles codoped with Yb and either Er, Hm, or Tm. To assess their suitability as dosimeter material, dose response, as well as its degree of fading over 50 up to 140 days after irradiation were measured. Fading rates as small as 5% in SrF2:Er,Yb and 4% in SrF2:Ho,Yb were derived, which are comparable to established dosimeter materials. A combination of spectroscopy, diffraction and DFT calculations was used to elucidate the effect of irradiation, pointing towards the formation of a secondary phase of Yb2+ that we predict could be Yb2OF2. This irreversible formation of a secondary phase is considered to be the explanation for the low fading behavior in SrF2-based phosphors compared to NaYF4:Yb, Er, a highly attractive feature for electron beam dosimetry. KW - DFT KW - Structure prediction PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-554115 DO - https://doi.org/10.1039/D2TC01773C SN - 2050-7526 VL - 10 IS - 32 SP - 11579 EP - 11587 PB - RSC CY - London AN - OPUS4-55411 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - George, Janine A1 - Naik, Aakash A1 - Ueltzen, Katharina T1 - Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry N2 - Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research. KW - Large Language Models KW - Materials Design KW - Materials Properties KW - Phonons KW - Bonding Analysis KW - Machine Learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622113 DO - https://doi.org/10.48550/arXiv.2411.15221 SP - 1 EP - 98 AN - OPUS4-62211 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Zimmermann, Yoel A1 - Bazgir, Adib A1 - Al-Feghali, Alexander A1 - Ansari, Mehrad A1 - Bocarsly, Joshua A1 - Brinson, L. Catherine A1 - Chiang, Yuan A1 - Circi, Defne A1 - Chiu, Min-Hsueh A1 - Daelman, Nathan A1 - Evans, Matthew L. A1 - Gangan, Abhijeet S. A1 - George, Janine A1 - Harb, Hassan A1 - Khalighinejad, Ghazal A1 - Khan, Sartaaj Takrim A1 - Klawohn, Sascha A1 - Lederbauer, Magdalena A1 - Mahjoubi, Soroush A1 - Mohr, Bernadette A1 - Moosavi, Seyed Mohamad A1 - Naik, Aakash A1 - Ozhan, Aleyna Beste A1 - Plessers, Dieter A1 - Roy, Aritra A1 - Schöppach, Fabian A1 - Schwaller, Philippe A1 - Terboven, Carla A1 - Ueltzen, Katharina A1 - Wu, Yue A1 - Zhu, Shang A1 - Janssen, Jan A1 - Li, Calvin A1 - Foster, Ian A1 - Blaiszik, Ben T1 - 34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery N2 - Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 34 total projects developed during the second annual Large Language Model Hackathon for Applications in Materials Science and Chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility. KW - Automation KW - LLM KW - Machine Learning KW - Agent KW - Bonding Analysis KW - Materials Searches KW - Finetuning PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-631720 DO - https://doi.org/10.48550/arXiv.2505.03049 SP - 1 EP - 33 AN - OPUS4-63172 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Amariamir, Sasan A1 - George, Janine A1 - Benner, Philipp T1 - SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction N2 - Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materials, remains a complex challenge due to the limitations of traditional heuristics and thermodynamic proxies. While stability metrics such as formation energy offer partial insights, they fail to account for kinetic factors and technological constraints that influence synthesis outcomes. These challenges are further compounded by the scarcity of negative data, as failed synthesis attempts are often unpublished or context-specific. We present SynCoTrain, a semi-supervised machine learning model designed to predict the synthesizability of materials. SynCoTrain employs a co-training framework leveraging two complementary graph convolutional neural networks: SchNet and ALIGNN. By iteratively exchanging predictions between classifiers, SynCoTrain mitigates model bias and enhances generalizability. Our approach uses Positive and Unlabeled (PU) learning to address the absence of explicit negative data, iteratively refining predictions through collaborative learning. The model demonstrates robust performance, achieving high recall on internal and leave-out test sets. By focusing on oxide crystals, a well-characterized material family with extensive experimental data, we establish SynCoTrain as a reliable tool for predicting synthesizability while balancing dataset variability and computational efficiency. This work highlights the potential of co-training to advance high-throughput materials discovery and generative research, offering a scalable solution to the challenge of synthesizability prediction. KW - Synthesizability KW - Accelerated Materials Design KW - Materials Acceleration Platforms KW - Materials Design KW - Materials Discovery KW - Co-training PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-637907 DO - https://doi.org/10.1039/D4DD00394B SN - 2635-098X VL - 4 IS - 6 SP - 1437 EP - 1448 PB - Royal Society of Chemistry (RSC) AN - OPUS4-63790 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Liu, Yuanbin A1 - Morrow, Joe D. A1 - Ertural, Christina A1 - Fragapane, Natascia L. A1 - Gardner, John L. A. A1 - Naik, Aakash A1 - Zhou, Yuxing A1 - George, Janine A1 - Deringer, Volker L. T1 - An automated framework for exploring and learning potential-energy surfaces N2 - Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex ('automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO2, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning -- with a long-term vision of making it a genuine mainstream tool in physics, chemistry, and materials science. KW - Machine learned interatomic potentials KW - Ab initio KW - DFT KW - Automation KW - Workflows KW - Machine learning KW - Materials design KW - Materials discovery PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623188 DO - https://doi.org/10.48550/arXiv.2412.16736 SN - 2331-8422 SP - 1 EP - 27 PB - Cornell University CY - Ithaca, NY AN - OPUS4-62318 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rosen, Andrew S. A1 - Gallant, Max A1 - George, Janine A1 - Riebesell, Janosh A1 - Sahasrabuddhe, Hrushikesh A1 - Shen, Jimmy-Xuan A1 - Wen, Mingjian A1 - Evans, Matthew L. A1 - Petretto, Guido A1 - Waroquiers, David A1 - Rignanese, Gian-Marco A1 - Persson, Kristin A. A1 - Jain, Anubhav A1 - Ganose, Alex M. T1 - Jobflow: Computational Workflows Made Simple N2 - We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future. KW - Automation KW - Workflow KW - Computational Materials Science KW - Computations KW - Software PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593104 DO - https://doi.org/10.21105/joss.05995 VL - 9 IS - 93 SP - 1 EP - 7 PB - The Open Journal AN - OPUS4-59310 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ghosh, Sheuly A1 - Ueltzen, Katharina A1 - George, Janine A1 - Neugebauer, Jörg A1 - Körmann, Fritz T1 - Chemical ordering and magnetism in face-centered cubic CrCoNi alloy N2 - AbstractThe impact of magnetism on chemical ordering in face-centered cubic CrCoNi medium entropy alloy is studied by a combination of ab initio simulations, machine learning potentials, and Monte Carlo simulations. Large magnetic energies are revealed for some mixed L12/L10 type ordered configurations, which are rooted in strong nearest-neighbor magnetic exchange interactions and chemical bonding among the constituent elements. There is a delicate interplay between magnetism and stability of MoPt2 and L12/L10 type of order, which may explain opposing experimental and theoretical findings. KW - Chemically complex materials KW - Alloys KW - Magnetism KW - Bonding analysis KW - Machine learning KW - Machine-learned interatomic potentials PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623114 DO - https://doi.org/10.1038/s41524-024-01439-8 SN - 2057-3960 VL - 10 IS - 1 SP - 1 EP - 10 PB - Springer Science and Business Media LLC AN - OPUS4-62311 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Amariamir, Sasan A1 - George, Janine A1 - Benner, Philipp T1 - SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction N2 - Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materials, remains a complex challenge due to the limitations of traditional heuristics and thermodynamic proxies. While stability metrics such as formation energy offer partial insights, they fail to account for kinetic factors and technological constraints that influence synthesis outcomes. These challenges are further compounded by the scarcity of negative data, as failed synthesis attempts are often unpublished or context-specific. We present SynCoTrain, a semi-supervised machine learning model designed to predict the synthesizability of materials. SynCoTrain employs a co-training framework leveraging two complementary graph convolutional neural networks: SchNet and ALIGNN. By iteratively exchanging predictions between classifiers, SynCoTrain mitigates model bias and enhances generalizability. Our approach uses Positive and Unlabeled (PU) Learning to address the absence of explicit negative data, iteratively refining predictions through collaborative learning. The model demonstrates robust performance, achieving high recall on internal and leave-out test sets. By focusing on oxide crystals, a well-characterized material family with extensive experimental data, we establish SynCoTrain as a reliable tool for predicting synthesizability while balancing dataset variability and computational efficiency. This work highlights the potential of co-training to advance high-throughput materials discovery and generative research, offering a scalable solution to the challenge of synthesizability prediction. KW - Materials Design KW - Materials Discovery KW - Synthesizability KW - Machine Learning KW - Cotraining KW - New Materials KW - Materials Acceleration Platforms PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622104 DO - https://doi.org/10.48550/arXiv.2411.12011 SP - 1 EP - 39 AN - OPUS4-62210 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - CONF A1 - George, Janine T1 - Materials design using chemical heuristics, workflows, and machine learning N2 - 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 - Group Seminar at the University of Toronto CY - Online Meeting DA - 18.07.2025 KW - Automation KW - Magnetism KW - High-throughput KW - Amorphous Materials KW - Phase Change Materials KW - Phonons PY - 2025 AN - OPUS4-63743 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - VIDEO 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 - Materials Design KW - Machine Learning KW - Chemical Bonding KW - Batteries KW - Amorphous Materials KW - Workflows KW - Machine Learned Interatomic Potentials KW - Phonons KW - Magnetism KW - Synthesizability PY - 2025 UR - https://www.youtube.com/watch?v=Sfco48s1fpU PB - YouTube, LLC CY - San Bruno, CA, USA AN - OPUS4-63744 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Grandel, Jonas A1 - George, Janine T1 - Mace-mp-03b Phonon Benchmark N2 - This repository contains phonon calculations and evaluations for the MACE MP-0b3 model. First release of the scripts used for the phonon benchmark in the paper benchmarking the MACE-MP-03b model. See https://arxiv.org/abs/2401.00096 for a previous version of the paper. Full Changelog: https://github.com/JaGeo/mace-mp-03b-phonon-benchmark/commits/v0.0.1 KW - Machine Learned Interatomic Potentials KW - Phonons KW - Thermal Conductivity KW - Materials Searches KW - Foundation Model PY - 2025 DO - https://doi.org/10.5281/zenodo.15462975 PB - Zenodo CY - Geneva AN - OPUS4-63174 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pallasch, Sebastian M. A1 - Bhosale, Manik A1 - Smales, Glen Jacob A1 - Schmidt, Caroline A1 - Riedel, Sibylle A1 - Zhao-Karger, Zhirong A1 - Esser, Birgit A1 - Dumele, Oliver T1 - Porous Azatruxene Covalent Organic Frameworks for Anion Insertion in Battery Cells N2 - Covalent organic frameworks (COFs) containing well-defined redox-active groups have become competitive materials for next-generation batteries. Although high potentials and rate performance can be expected, only a few examples of p-type COFs have been reported for charge storage to date with even fewer examples on the use of COFs in multivalent ion batteries. Herein, we report the synthesis of a p-type highly porous and crystalline azatruxene-based COF and its application as a positive electrode material in Li- and Mg-based batteries. When this material is used in Li-based half cells as a COF/carbon nanotube (CNT) electrode, a discharge potential of 3.9 V is obtained with discharge capacities of up to 70 mAh g−1 at a 2 C rate. In Mg batteries using a tetrakis(hexafluoroisopropyloxy)borate electrolyte, cycling proceeds with an averge discharge voltage of 2.9 V. Even at a fast current rate of 5 C, the capacity retention amounts to 84% over 1000 cycles. KW - COFs PY - 2024 DO - https://doi.org/10.1021/jacs.4c04044 VL - 146 IS - 25 SP - 17318 EP - 17324 PB - Journal of the American Chemical Society AN - OPUS4-60419 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 3D microstructural information 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. Our work suggests a methodology for the identification, authentication, and traceability of AM components using microstructural features in AM components. We will show a workflow that includes analysing 3D micro computed tomography data and selecting a set number of voids that fulfil the identification criteria. We will show the results this workflow produces for a series of 20 Al-based cuboid samples with identical processing parameters and discuss their prospects and limitations. The workflow can help to establish a non-tamperable connection between an additively manufactured part and its digital data and hence link the physical and the digital world. T2 - MSE Konferenz CY - Darmstadt, Germany DA - 24.09.2024 KW - Additive Manufacturing KW - Fingerprint KW - Computed tomography PY - 2024 AN - OPUS4-62288 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hildebrandt, R. A1 - Seifert, M. A1 - George, Janine A1 - Blaurock, S. A1 - Botti, S. A1 - Krautscheid, H. A1 - Grundmann, M. A1 - Sturm, C. T1 - Determination of acoustic phonon anharmonicities via second-order Raman scattering in CuI N2 - We demonstrate the determination of anharmonic acoustic phonon properties via second-order Raman scattering exemplarily on copper iodide single crystals. The origin of multi-phonon features from the second-order Raman spectra was assigned by the support of the calculated 2-phonon density of states. In this way, the temperature dependence of acoustic phonons was determined down to 10 K. To determine independently the harmonic contributions of respective acoustic phonons, density functional theory in quasi-harmonic approximation was used. Finally, the anharmonic contributions were determined. The results are in agreement with earlier publications and extend CuI’s determined acoustic phonon properties to lower temperatures with higher accuracy. This approach demonstrates that it is possible to characterize the acoustic anharmonicities via Raman scattering down to zero-temperature renormalization constants of at least 0.1 cm⁻¹. KW - Thermoelectrics KW - Thermal transport KW - Phonon interactions KW - Density functional theory KW - Phonons PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593164 DO - https://doi.org/10.1088/1367-2630/ad1141 SN - 1367-2630 VL - 25 IS - 12 SP - 1 EP - 12 PB - IOP Publishing CY - London AN - OPUS4-59316 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 - INPR A1 - Hildebrandt, R. A1 - Seifert, M. A1 - George, Janine A1 - Blaurock, S. A1 - Botti, S. A1 - Krautscheid, H. A1 - Grundmann, M. A1 - Sturm, C. T1 - Determination of acoustic phonon anharmonicities via second-order Raman scattering in CuI N2 - We demonstrate the determination of anharmonic acoustic phonon properties via second-order Raman scattering exemplarily on copper iodide single crystals. The origin of multi-phonon features from the second-order Raman spectra was assigned by the support of the calculated 2-phonon density of states. In this way, the temperature dependence of acoustic phonons was determined down to 10 K. To determine independently the harmonic contributions of respective acoustic phonons, density functional theory (DFT) in quasi-harmonic approximation was used. Finally, the anharmonic contributions were determined. The results are in agreement with earlier publications and extend CuI's determined acoustic phonon properties to lower temperatures with higher accuracy. This approach demonstrates that it is possible to characterize the acoustic anharmonicities via Raman scattering down to zero-temperature renormalization constants of at least 0.1 cm⁻¹. KW - DFT KW - Quasi-harmonic approximation KW - Raman KW - Phonons KW - Anharmonicity PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-583699 DO - https://doi.org/10.48550/arXiv.2305.18931 SN - 2331-8422 SP - 1 EP - 14 PB - Cornell University CY - Ithaca, NY AN - OPUS4-58369 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 - GEN A1 - Ghosh, Sheuly A1 - Ueltzen, Katharina A1 - George, Janine A1 - Neugebauer, Jörg A1 - Körmann, Fritz T1 - Chemical ordering and magnetism in face-centered cubic CrCoNi alloy N2 - The impact of magnetism on chemical ordering in face-centered cubic CrCoNi medium entropy alloy is studied by a combination of ab initio simulations, machine learning potentials, and Monte Carlo simulations. Large magnetic energies are revealed for some mixed L12 /L10 type ordered configurations, which are rooted in strong nearest-neighbor magnetic exchange interactions and chemical bonding among the constituent elements. There is a delicate interplay between magnetism and stability of MoPt2 and L12 /L10 type of ordering which may explain opposing experimental and theoretical findings. KW - Bonding analysis KW - Alloys KW - Magnetism KW - Material design PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-600040 DO - https://doi.org/10.21203/rs.3.rs-3978660/v1 SN - 2693-5015 SP - 1 EP - 13 PB - Research Square Platform LLC CY - Durham, NC AN - OPUS4-60004 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -