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 - JOUR A1 - Anker, Andy S. A1 - Aspuru-Guzik, Alán A1 - Ben Mahmoud, Chiheb A1 - Bennett, Sophie A1 - Briling, Ksenia R. A1 - Changiarath, Arya A1 - Chong, Sanggyu A1 - Collins, Christopher M. A1 - Cooper, Andrew I. A1 - Crusius, Daniel A1 - Darmawan, Kevion K. A1 - Das, Basita A1 - David, Nicholas A1 - Day, Graeme M. A1 - Deringer, Volker L. A1 - Duarte, Fernanda A1 - Eardley-Brunt, Annabel A1 - Evans, Matthew L. A1 - Evans, Rob A1 - Fairlamb, Ian A1 - Franklin, Barnabas A. A1 - Frey, Jeremy A1 - Ganose, Alex M. A1 - Goulding, Mark A1 - Hafizi, Roohollah A1 - Hakkennes, Matthijs A1 - Hickey, Niamh A1 - James, Gillian A1 - Jelfs, Kim E. A1 - Kalikadien, Adarsh V. A1 - Kapil, Venkat A1 - Koczor-Benda, Zsuzsanna A1 - Krammer, Ferdinand A1 - Kulik, Heather J. A1 - Kumar, Vishank A1 - Kuttner, Christian A1 - Lam, Erwin A1 - Lou, Yuchen A1 - Mante, Eltjo A1 - Martin, Jennie A1 - Mroz, Austin M. A1 - Nematiaram, Tahereh A1 - Pare, Charles W. P. A1 - Patra, Sarbani A1 - Proudfoot, James A1 - Ruscic, Branko A1 - Ryder, Matthew R. A1 - Sakaushi, Ken A1 - Saßmannshausen, Jörg A1 - Savoie, Brett M. A1 - Schneider, Nadine A1 - Schwaller, Philippe A1 - Skjelstad, Bastian Bjerkem A1 - Sun, Wenhao A1 - Szczypiński, Filip T. A1 - Torrisi, Steven A1 - Ueltzen, Katharina A1 - Vishnoi, Shubham A1 - Walsh, Aron A1 - Wang, Xinwei A1 - Wilson, Chloe A1 - Wu, Ruiqi A1 - Zeitler, Jakob T1 - Discovering structure–property correlations: General discussion N2 - This article is a discussion of the paper "Web-BO: Towards increased accessibility of Bayesian optimisation (BO) for chemistry" by Austin M. Mroz, Piotr N. Toka, Ehecatl Antonio del Río Chanona and Kim E. Jelfs (Faraday discussions, 2025, 256, 221-234). KW - Materials design KW - Machine learning KW - Automation KW - Materials discovery PY - 2025 DO - https://doi.org/10.1039/d4fd90062f SN - 1359-6640 SN - 1364-5498 VL - 256 IS - Themed collection: Data-driven discovery in the chemical sciences SP - 373 EP - 412 PB - Royal Society of Chemistry (RSC) CY - Cambridge AN - OPUS4-62208 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR 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 A1 - Gangan, Abhijeet S A1 - George, Janine A1 - Harb, Hassan A1 - Khalighinejad, Ghazal A1 - Takrim Khan, Sartaaj A1 - Klawohn, Sascha A1 - Lederbauer, Magdalena A1 - Mahjoubi, Soroush A1 - Mohr, Bernadette A1 - Mohamad Moosavi, Seyed A1 - Naik, Aakash Ashok A1 - Ozhan, Aleyna Beste A1 - Plessers, Dieter A1 - Roy, Aritra A1 - Schoeppach, 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 - 32 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 - Large Language Models KW - Machine Learning KW - Materials Design KW - Bonding Analysis KW - Phonons KW - Thermal properties PY - 2025 DO - https://doi.org/10.1088/2632-2153/ae011a SN - 2632-2153 VL - 6 IS - 3 SP - 1 EP - 34 PB - IOP Publishing AN - OPUS4-64019 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bustamante, Joana A1 - Ghata, Anupama A1 - Naik, Aakash A. A1 - Ertural, Christina A1 - Ueltzen, Katharina A1 - Zeier, Wolfgang G. A1 - George, Janine T1 - Thermal transport in Ag8T S6(T = Si, Ge, Sn) argyrodites: an integrated experimental, quantum-chemical, and computational modelling study N2 - Argyrodite-type Ag-based sulfides combine exceptionally low lattice thermal and high ionic conductivity, making them promising candidates for thermoelectric and solid-state energy applications. In this work, we studied Ag8TS6 (T = Si, Ge, Sn) argyrodite family by combining chemical-bonding analysis, lattice vibrational properties simulation, and experimental measurements to investigate their structural and thermal transport properties. Furthermore, we propose a two-channel lattice-dynamics model based on Grüneisen-derived phonon lifetimes and compare it to an approach using machine-learned interatomic potentials. Both approaches are able to predict thermal conductivity in agreement with experimental lattice thermal conductivities along the whole temperature range, highlighting their potential suitability for future high-throughput predictions. Our findings also reveal a relationship between bond heterogeneity arising from weakly bonded Ag+ ions and occupied antibonding states in Ag–S and Ag–Ag interactions and strong anharmonicity, including large Grüneisen parameters, and low sound velocities, which are responsible for the low lattice thermal conductivity of Ag8SnS6, Ag8GeS6, and Ag8SiS6. We furthermore show that thermal and ionic conductivities in all three compounds are independent of each other and can likely be tuned individually. KW - Thermal Conductivity KW - Ionic Conductivity KW - Machine Learning KW - Materials Design KW - Microstructure KW - Bonding Analysis KW - Anharmonicity PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-657076 DO - https://doi.org/10.1039/D5TA08709K SN - 2050-7488 SP - 1 EP - 13 PB - Royal Society of Chemistry (RSC) AN - OPUS4-65707 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -