AI for Science Social

AI for Science is rapidly emerging as a key area where machine learning can accelerate discovery in domains such as materials science, biology, physics, and mathematics. Progress in this space increasingly depends on collaboration between machine learning researchers and domain scientists, as well as open ecosystems of data, models, and tools. This socials aims to create an informal space at ICLR for researchers interested in AI for Science and open collaboration to connect, exchange ideas, and build new collaborations.

The socials will bring together participants from several ICLR workshops related to AI for Science, including AI4Mat, FM4Science, AI&PDE, and Sci4DL, and foster interaction across these communities. The session will focus on practical challenges and opportunities in building open scientific ecosystems, including open datasets and benchmarks, open-source tools and foundation models for science, cross-disciplinary collaboration, and community-driven initiatives.

The format will emphasize interaction and networking with brief opening remarks, structured speed networking, themed small-group discussions, and a short open panel conversation where participants can share insights and identify opportunities for collaboration. The social will also provide a welcoming environment for students and early-career researchers to engage with both academic and industry researchers working on AI-driven scientific discovery