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    <title language="eng">Machine learning‐enabled tomographic imaging of chemical short‐range atomic ordering</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Advanced materials</parentTitle>
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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\u2010enhanced atom probe tomography (APT) can mine the near\u2010atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium\u2010entropy alloy. Multiple CSRO configurations are revealed, with their formation supported by state\u2010of\u2010the\u2010art Monte\u2010Carlo simulations. Quantitative analysis of these CSROs allows establishing relationships between processing parameters and physical properties. 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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Yue Li</author>
    <author>Timoteo Colnaghi</author>
    <author>Yilun Gong</author>
    <author>Huaide Zhang</author>
    <author>Yuan Yu</author>
    <author>Ye Wei</author>
    <author>Bin Gan</author>
    <author>Min Song</author>
    <author>Andreas Marek</author>
    <author>Markus Rampp</author>
    <author>Siyuan Zhang</author>
    <author>Zongrui Pei</author>
    <author>Matthias Wuttig</author>
    <author>Sheuly Ghosh</author>
    <author>Fritz Körmann</author>
    <author>Jörg Neugebauer</author>
    <author>Zhangwei Wang</author>
    <author>Baptiste Gault</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Chemical short-range order (CSRO)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Atom probe tomography (APT)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">6 Materialchemie</collection>
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    <title language="eng">32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery</title>
    <abstract language="eng">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.</abstract>
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    <author>Janine George</author>
    <author>Hassan Harb</author>
    <author>Ghazal Khalighinejad</author>
    <author>Sartaaj Takrim Khan</author>
    <author>Sascha Klawohn</author>
    <author>Magdalena Lederbauer</author>
    <author>Soroush Mahjoubi</author>
    <author>Bernadette Mohr</author>
    <author>Seyed Mohamad Moosavi</author>
    <author>Aakash Ashok Naik</author>
    <author>Aleyna Beste Ozhan</author>
    <author>Dieter Plessers</author>
    <author>Aritra Roy</author>
    <author>Fabian Schoeppach</author>
    <author>Philippe Schwaller</author>
    <author>Carla Terboven</author>
    <author>Katharina Ueltzen</author>
    <author>Yue Wu</author>
    <author>Shang Zhu</author>
    <author>Jan Janssen</author>
    <author>Calvin Li</author>
    <author>Ian Foster</author>
    <author>Ben Blaiszik</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Large Language Models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Materials Design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bonding Analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Phonons</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Thermal properties</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">6 Materialchemie</collection>
    <collection role="institutes" number="">6.6 Digitale Materialchemie</collection>
    <collection role="themenfelder" number="">Material</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="themenfelder" number="">Materialdesign</collection>
  </doc>
</export-example>
