14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Abstract

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models 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 this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

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Cited by in corpus (13)

• Minireview on Disordered Optical Metasurfaces
• Materials science in the era of large language models: a perspective
• Image and Data Mining in Reticular Chemistry Using GPT-4V
• Generative AI and Process Systems Engineering: The Next Frontier
• Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
• Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials
• From Text to Test: AI-Generated Control Software for Materials Science Instruments
• Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
• Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models
• HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion
• Dara: Automated multiple-hypothesis phase identification and refinement from powder X-ray diffraction
• aLLoyM: A large language model for alloy phase diagram prediction
• GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols