34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery

• URL: http://arxiv.org/abs/2505.03049v2
• Date: Fri, 16 May 2025 01:37:58 GMT
• Title: 34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
• Authors: Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali, Mehrad Ansari, Joshua Bocarsly, L. Catherine Brinson, Yuan Chiang, Defne Circi, Min-Hsueh Chiu, Nathan Daelman, Matthew L. Evans, Abhijeet S. Gangan, Janine George, Hassan Harb, Ghazal Khalighinejad, Sartaaj Takrim Khan, Sascha Klawohn, Magdalena Lederbauer, Soroush Mahjoubi, Bernadette Mohr, Seyed Mohamad Moosavi, Aakash Naik, Aleyna Beste Ozhan, Dieter Plessers, Aritra Roy, Fabian Schöppach, Philippe Schwaller, Carla Terboven, Katharina Ueltzen, Yue Wu, Shang Zhu, Jan Janssen, Calvin Li, Ian Foster, Ben Blaiszik,
• Abstract summary: Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research.<n>Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data.<n>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.
• Score: 27.265303690468272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Towards Artificial Intelligence Research Assistant for Expert-Involved Learning [64.7438151207189]
  Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have emerged as transformative tools in scientific research.<n>We present textbfARtificial textbfIntelligence research assistant for textbfExpert-involved textbfLearning (ARIEL)
  arXiv  Detail & Related papers  (2025-05-03T14:21:48Z)
• Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents [11.74019905854637]
  Large language models (LLMs) are evolving into scientific agents that automate critical tasks.<n>Unlike general-purpose LLMs, specialized agents integrate domain-specific knowledge, advanced tool sets, and robust validation mechanisms.<n>We highlight why they differ from general agents and the ways in which they advance research across various scientific fields.
  arXiv  Detail & Related papers  (2025-03-31T13:11:28Z)
• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations.<n>This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• From Generalist to Specialist: A Survey of Large Language Models for Chemistry [14.317448405387195]
  Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP)<n>The predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry.<n>Although several studies have reviewed Pretrained Language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs.
  arXiv  Detail & Related papers  (2024-12-28T03:40:25Z)
• Foundational Large Language Models for Materials Research [22.77591279242839]
  Large Language Models (LLMs) offer opportunities to accelerate materials research through automated analysis and prediction.<n>Here, we present LLaMat, a family of foundational models for materials science developed through continued pretraining of LLaMA models.<n>We demonstrate that LLaMat excels in materials-specific NLP and structured information extraction while maintaining general linguistic capabilities.
  arXiv  Detail & Related papers  (2024-12-12T18:46:38Z)
• Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry [68.97388926441971]
  We present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry.<n>The event engaged participants across global hybrid locations, resulting in 34 team submissions.<n>The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications.
  arXiv  Detail & Related papers  (2024-11-20T23:08:01Z)
• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  Large language models (LLMs) have revolutionized the way text and other modalities of data are handled. We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
  We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects. MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years. We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
  arXiv  Detail & Related papers  (2024-06-10T15:19:09Z)
• Scientific Large Language Models: A Survey on Biological & Chemical Domains [47.97810890521825]
  Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration.
  arXiv  Detail & Related papers  (2024-01-26T05:33:34Z)
• 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon [30.978561315637307]
  Large-language models (LLMs) could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of the hackathon.
  arXiv  Detail & Related papers  (2023-06-09T22:22:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.