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 -