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 - Batatia, Ilyes A1 - Benner, Philipp A1 - Chiang, Yuan A1 - Elena, Alin M. A1 - Kovács, Dávid P. A1 - Riebesell, Janosh A1 - Advincula, Xavier R. A1 - Asta, Mark A1 - Avaylon, Matthew A1 - Baldwin, William J. A1 - Berger, Fabian A1 - Bernstein, Noam A1 - Bhowmik, Arghya A1 - Bigi, Filippo A1 - Blau, Samuel M. A1 - Cărare, Vlad A1 - Ceriotti, Michele A1 - Chong, Sanggyu A1 - Darby, James P. A1 - De, Sandip A1 - Della Pia, Flaviano A1 - Deringer, Volker L. A1 - Elijošius, Rokas A1 - El-Machachi, Zakariya A1 - Fako, Edvin A1 - Falcioni, Fabio A1 - Ferrari, Andrea C. A1 - Gardner, John L. A. A1 - Gawkowski, Mikołaj J. A1 - Genreith-Schriever, Annalena A1 - George, Janine A1 - Goodall, Rhys E. A. A1 - Grandel, Jonas A1 - Grey, Clare P. A1 - Grigorev, Petr A1 - Han, Shuang A1 - Handley, Will A1 - Heenen, Hendrik H. A1 - Hermansson, Kersti A1 - Ho, Cheuk Hin A1 - Hofmann, Stephan A1 - Holm, Christian A1 - Jaafar, Jad A1 - Jakob, Konstantin S. A1 - Jung, Hyunwook A1 - Kapil, Venkat A1 - Kaplan, Aaron D. A1 - Karimitari, Nima A1 - Naik, Aakash A. A1 - Csányi, Gábor T1 - A foundation model for atomistic materials chemistry N2 - Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields. KW - Materials Design KW - Thermal Conducitivity KW - Nanoparticles KW - Batteries PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-647829 DO - https://doi.org/10.1063/5.0297006 SN - 0021-9606 VL - 163 IS - 18 SP - 1 EP - 89 PB - AIP Publishing AN - OPUS4-64782 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Joseph, A. A1 - Bernardes, C. E. S. A1 - Druzhinina, A. I. A1 - Varushchenko, R. M. A1 - Nguyen, Thi Yen A1 - Emmerling, Franziska A1 - Yuan, L. A1 - Dupray, V. A1 - Coquerel, G. A1 - Minas da Piedade, M. E. T1 - Polymorphic phase transition in 4′-hydroxyacetophenone: Equilibrium temperature, kinetic barrier, and the relative stability of Z′=1 and Z′=2 forms N2 - Particularly relevant in the context of polymorphism is understanding how structural, thermodynamic, and kinetic factors dictate the stability domains of polymorphs, their tendency to interconvert through phase transitions, or their possibility to exist in metastable states. These three aspects were investigated here for two 4′-hydroxyacetophenone (HAP) polymorphs, differing in crystal system, space group, and number and conformation of molecules in the asymmetric unit. The results led to a ΔfGm°-T phase diagram highlighting the enantiotropic nature of the system and the fact that the Z′=1 polymorph is not necessarily more stable than its Z′=2 counterpart. It was also shown that the form II → form I transition is entropy driven and is likely to occur through a nucleation and growth mechanism, which does not involve intermediate phases, and is characterized by a high activation energy. Finally, although it has been noted that conflicts between hydrogen bond formation and close packing are usually behind exceptions from the hypothesis of Z′=1 forms being more stable than their higher Z′ analogues, in this case, the HAP polymorph with stronger hydrogen bonds (Z′=2) is also the one with higher density. KW - Polymorphism KW - Polymorphic transition KW - 4'-hydroxyacetophenone PY - 2017 DO - https://doi.org/10.1021/acs.cgd.6b01876 SN - 1528-7483 SN - 1528-7505 VL - 17 IS - 4 SP - 1918 EP - 1932 PB - ACS AN - OPUS4-40167 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Steinkönig, J. A1 - Bloesser, F. R. A1 - Huber, B. A1 - Welle, A. A1 - Trouillet, V. A1 - Weidner, Steffen A1 - Barner, L. A1 - Roesky, P. W. A1 - Yuan, J. A1 - Goldmann, A. S. A1 - Barner-Kowollik, C. T1 - Controlled radical polymerization and in-depth mass-spectrometric characterization of poly(ionic liquid)s and their photopatterning on surfaces N2 - The preparation and characterization of poly(ionic liquid)s (PILs) bearing a polystyrene backbone via reversible addition fragmentation chain transfer (RAFT) polymerization and their photolithographic patterning on silicon wafers is reported. The controlled radical polymerization of the styrenic ionic liquid (IL) monomers ([BVBIM]X, X = Cl− or Tf2N−) by RAFT polymerization is investigated in detail. We provide a general synthetic tool to access this class of PILs with controlled molecular weight and relatively narrow molecular weight distribution (2000 g mol−1 ≤ Mn ≤ 10 000 g mol−1 with dispersities between 1.4 and 1.3 for p([BVBIM]Cl); 2100 g mol−1 ≤ MP ≤ 14 000 g mol−1 for p([BVBIM]Tf2N)). More importantly, we provide an in-depth characterization of the PILs and demonstrate a detailed mass spectrometric analysis via matrix-assisted laser desorption ionization (MALDI) as well as – for the first time for PILs – electrospray ionization mass spectrometry (ESI-MS). Importantly, p([BVBIM]Cl) and p([DMVBIM]Tf2N) were photochemically patterned on silicon wafers. Therefore, a RAFT agent carrying a photoactive group based on ortho-quinodimethane chemistry – more precisely photoenol chemistry – was photochemically linked for subsequent controlled radical polymerization of [BVBIM]Cl and [DMVBIM]Tf2N. The successful spatially-resolved photografting is evidenced by surface-sensitive characterization methods such as X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). The presented method allows for the functionalization of diverse surfaces with poly(ionic liquid)s. KW - reversible addition fragmentation chain transfer (RAFT) polymerization KW - polyionic liquids KW - mass spectrometry KW - surface modification PY - 2016 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-355202 DO - https://doi.org/10.1039/C5PY01320H VL - 7 SP - 451 EP - 461 PB - Royal Society of Chemistry CY - Cambridge AN - OPUS4-35520 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 -