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  3. OSI Hackathon – Berlin Site, co-organized by Dunia, FAIRmat and HZB

OSI Hackathon – Berlin Site, co-organized by Dunia, FAIRmat and HZB

lumaHosted on Luma

Fetched 44 minutes ago

Wednesday, October 21, 2026

to Thursday, October 22, 2026

•

1 day long

Artificial IntelligenceData ScienceFintech

in person

Event Type

17

Participants

1

Est. Projects

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About this hackathon

On October 21–22, 2026, Berlin joins the Open Scientific Intelligence (OSI) Hackathon as a local on-site hub (https://llmhackathon.github.io/). OSI is a free, hybrid, international hackathon that runs at the same time across sites worldwide, alongside a virtual cohort. It was previously called the LLM Hackathon for Materials Science & Chemistry, which Kevin Jablonka helped launch in 2023. That first edition produced 14 LLM applications in a single sprint, written up in a peer-reviewed paper in Digital Discovery. The event has grown every year since: 34 projects across 7 sites in 2024, and 120 projects across 16 sites in 2025, when agentic workflows and lab automation were the main focus. For its 2026 edition, the hackathon is broadening beyond materials science and chemistry to cover the physical sciences and mathematics as a whole. Projects can be built around LLMs, autonomous agents, datasets, benchmarks, models or scientific software. The Berlin hub brings together three partners. Kevin Jablonka contributes his experience as one of the hackathon's founders. FAIRmat contributes its expertise in FAIR research-data infrastructure for materials science. Dunia contributes its expertise in autonomous AI-driven materials discovery. HZB contributes challenging materials questions. Together they give participants real scientific problems and data to work on, plus mentors from both academia and industry. Students, researchers and professionals of any background are welcome, and participation is free. Teams usually have 3–10 people. Over two days they build a working prototype, and projects are judged on potential for impact, innovativeness, scalability and scientific relevance.

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