English

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Computers and Society 2025-06-24 v1 Distributed, Parallel, and Cluster Computing Physics and Society

Abstract

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Keywords

Cite

@article{arxiv.2506.17510,
  title  = {A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery},
  author = {Rafael Ferreira da Silva and Milad Abolhasani and Dionysios A. Antonopoulos and Laura Biven and Ryan Coffee and Ian T. Foster and Leslie Hamilton and Shantenu Jha and Theresa Mayer and Benjamin Mintz and Robert G. Moore and Salahudin Nimer and Noah Paulson and Woong Shin and Frederic Suter and Mitra Taheri and Michela Taufer and Newell R. Washburn},
  journal= {arXiv preprint arXiv:2506.17510},
  year   = {2025}
}
R2 v1 2026-07-01T03:27:31.811Z