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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2505.03049 |
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| _version_ | 1866909611826610176 |
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| author | Zimmermann, Yoel Bazgir, Adib Al-Feghali, Alexander Ansari, Mehrad Bocarsly, Joshua Brinson, L. Catherine Chiang, Yuan Circi, Defne Chiu, Min-Hsueh Daelman, Nathan Evans, Matthew L. Gangan, Abhijeet S. George, Janine Harb, Hassan Khalighinejad, Ghazal Khan, Sartaaj Takrim Klawohn, Sascha Lederbauer, Magdalena Mahjoubi, Soroush Mohr, Bernadette Moosavi, Seyed Mohamad Naik, Aakash Ozhan, Aleyna Beste Plessers, Dieter Roy, Aritra Schöppach, Fabian Schwaller, Philippe Terboven, Carla Ueltzen, Katharina Wu, Yue Zhu, Shang Janssen, Jan Li, Calvin Foster, Ian Blaiszik, Ben |
| author_facet | Zimmermann, Yoel Bazgir, Adib Al-Feghali, Alexander Ansari, Mehrad Bocarsly, Joshua Brinson, L. Catherine Chiang, Yuan Circi, Defne Chiu, Min-Hsueh Daelman, Nathan Evans, Matthew L. Gangan, Abhijeet S. George, Janine Harb, Hassan Khalighinejad, Ghazal Khan, Sartaaj Takrim Klawohn, Sascha Lederbauer, Magdalena Mahjoubi, Soroush Mohr, Bernadette Moosavi, Seyed Mohamad Naik, Aakash Ozhan, Aleyna Beste Plessers, Dieter Roy, Aritra Schöppach, Fabian Schwaller, Philippe Terboven, Carla Ueltzen, Katharina Wu, Yue Zhu, Shang Janssen, Jan Li, Calvin Foster, Ian Blaiszik, Ben |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery Zimmermann, Yoel Bazgir, Adib Al-Feghali, Alexander Ansari, Mehrad Bocarsly, Joshua Brinson, L. Catherine Chiang, Yuan Circi, Defne Chiu, Min-Hsueh Daelman, Nathan Evans, Matthew L. Gangan, Abhijeet S. George, Janine Harb, Hassan Khalighinejad, Ghazal Khan, Sartaaj Takrim Klawohn, Sascha Lederbauer, Magdalena Mahjoubi, Soroush Mohr, Bernadette Moosavi, Seyed Mohamad Naik, Aakash Ozhan, Aleyna Beste Plessers, Dieter Roy, Aritra Schöppach, Fabian Schwaller, Philippe Terboven, Carla Ueltzen, Katharina Wu, Yue Zhu, Shang Janssen, Jan Li, Calvin Foster, Ian Blaiszik, Ben Machine Learning Materials Science 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. |
| title | 34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2505.03049 |