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 - TY - JOUR A1 - Naik, Aakash A1 - Ueltzen, Katharina A1 - Ertural, Christina A1 - Jackson, Adam J. A1 - George, Janine T1 - LobsterPy: A package to automatically analyze LOBSTERruns N2 - The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API. KW - Materials Science KW - Automation KW - Bonding Analysis KW - Materials Properties PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-595809 DO - https://doi.org/10.21105/joss.06286 VL - 9 IS - 94 SP - 1 EP - 4 PB - The Open Journal AN - OPUS4-59580 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Automation in Computational Materials Science N2 - The talk „Automation in computational materials science“ deals with the current state of automation in the field of computational materials science. It illustrates how automation can, for example, be used to speed up the search for new ferroelectric materials and spintronic materials. Furthermore, it lists current tools for automation and challenges in the field. T2 - SALSA School 2021 CY - Online meeting DA - 16.09.2021 KW - Automation KW - High-throughput PY - 2021 AN - OPUS4-53483 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -