where AI acceleratesSCIENCE
THE MISSION
Uniting the world's brightest minds to build intelligent agents that don't just analyse data but actively collaborate in the research process—accelerating breakthroughs in humanity's most critical fields.
THE ECOSYSTEM
Our open platform spans the entire AI-for-science development lifecycle—fostering collaborative events, open-source infrastructure, and shared evaluation standards.
Connect
ai4.science brings together researchers, developers, and AI experts through open collaborative events. Our hackathons foster innovation in AI-human scientific collaboration, while future markathons will focus on creating shared benchmarks for open AI evaluation in scientific domains.
Build
mcp.science provides the open-source infrastructure and tools researchers need. Our Model Context Protocol servers offer the technical foundation for building AI agents that truly understand and contribute to scientific research workflows.
Test
bench.science establishes open evaluation frameworks for AI capabilities in scientific research. Our community-driven benchmarks ensure AI systems can reason effectively about complex scientific problems and contribute meaningfully to collaborative research outcomes.
Next Event
Hackathon · Beijing
Join us at Tsinghua University to explore innovative paths in human-AI collaboration. This hackathon challenges developers and researchers to build AI agents for science within the Model-Context-Protocol framework.
Dates & Location
Aug 22–24 2025
Physics Building, Tsinghua University
Application Deadline
August 15 2025
Organizers
Innovation Showcase
Inaugural Event: Stanford Quantum Science Hackathon
Our first event at Stanford University brought together researchers from quantum science and engineering to build applications where AI agents contribute meaningfully to real scientific tasks—from computation and simulation to experimental control and ideation.
CQED-Design-AI
1st Place Winner
Closed-loop planar circuit QED design with LLM - revolutionizing quantum circuit optimization through AI-driven design iterations.
Team: Matthew Chalk, Eesh Gupta, Kaveh Pezeshki, Yueheng Shi, Wendy Wan
EXPT - Cursor for Experiments
2nd Place Winner
An intelligent interface for experimental physics, bringing cursor-like AI assistance to laboratory experiment design and execution.
Team: Nelson Ooi
Self-Evolving MCP for Quantum-ManyBody
3rd Place Winner
A framework of self-evolving MCP for scientific Python libraries, demonstrated on quantum many-body physics computations.
Team: Wanda Hou, Hongye Hu, Miao Li