Autonomous Science Network

Roadmap

Community roadmap for interconnected autonomous labs

This roadmap outlines a community-driven strategy to connect autonomous laboratories across institutions, standardize interfaces for interoperability, and enable intelligent orchestration of experiments and data. The goal is to build a national infrastructure for distributed, automated, and reproducible science.

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Autonomous science roadmap

Roadmap pillars

Instrument and cyberinfrastructure integration

Enable agents to operate diverse scientific instruments and computational resources across institutions.

  • M1. Common integration interfaces with vendor-agnostic hardware abstraction.
  • M2. End-to-end autonomous workflows across institutions and facilities.
  • M3. Federated cyberinfrastructure with adaptive resource management and digital twins.
  • M4. Scalable national framework for real-time data flows and human override.

Agent-driven data management

Shift from passive storage to intelligent, agent-managed data lifecycles that enforce FAIR principles.

  • M5. AI-driven metadata systems with automated annotation.
  • M6. Federated data mesh architecture with common APIs.
  • M7. Real-time data processing with provenance tracking and compliance.

AI agent-driven orchestration

Agents coordinate experiments using AI methods grounded in scientific knowledge.

  • M8. Hierarchical agent architectures that orchestrate traditional methods.
  • M9. Knowledge integration across facilities with real-time insight propagation.

Interoperable communication standards

Standardize protocols for secure, fault-tolerant coordination between autonomous agents.

  • M10. Containerized agent microservices with gRPC/AMQP messaging.
  • M11. Zero-trust communication infrastructure with continuous authentication.
  • M12. Self-discovering agent networks with dynamic capability negotiation.

Education and workforce development

Prepare scientists to collaborate with autonomous systems and oversee AI-driven workflows.

  • M13. National autonomous science education consortium.
  • M14. Immersive virtual labs and human-AI collaboration assessment.

Roadmap paper

Ferreira da Silva, Rafael, Milad Abolhasani, Dionysios A. Antonopoulos, Laura Biven, Ryan Coffee, Ian T. Foster, Leslie Hamilton et al. "A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery." arXiv preprint arXiv:2506.17510 (2025).