Summary
Community report cataloguing and categorizing the projects of the 2025 LLM Hackathon for Materials Science and Chemistry into knowledge-synthesis and action/execution applications, and outlining directions toward autonomous, self-driving research workflows.
Keywords
large language modelshackathonmaterials sciencechemistryagentic workflowscommunity reportself-driving laboratories
Cite
@article{2026_roy_llm_hackathon_materials_chemistry,
title = {From Knowledge to Action: Outcomes of the 2025 Large Language Models (LLM) Hackathon for Applications in Materials Science and Chemistry},
author = {2025 LLM Hackathon for Applications in Materials Science and Chemistry Participants},
journal = {arXiv:2605.03205 (2026)},
year = {2026},
eprint = {2605.03205},
archivePrefix = {arXiv}
}