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Below is a summary assembled by the Research & Innovation Office (RIO). Please see the full solicitation for complete information about the funding opportunity.

Program Summary

Autonomous experimentation is poised to accelerate research and unlock critical scientific advances that bolster U.S. competitiveness and address pressing societal needs. Programmable Cloud Laboratories can execute automated workstreams, including self-driving lab workflows, to efficiently move research goals through artificial intelligence (AI) enabled experiment design, laboratory preparations, data collection, data analysis and interpretation. While limited-scale efforts have shown promise, versatile programmable and self-driving labs capable of addressing complex research questions with trustworthy results will require coordinated technological advances and an engaged research community. Additional challenges include the availability of automated laboratory infrastructure, standardized approaches to data collection for interoperability, advances in AI for data interpretation and experimental design, and more. This solicitation aims to address such gaps and realize the potential of autonomous experimentation.

The Test Bed: Toward a Network of Programmable Cloud Laboratories (PCL Test Bed) program seeks to establish and facilitate the operation of distributed autonomous laboratory facilities. These laboratories will combine technological and human capacity to enable integration, testing, evaluation, validation, and translation of cutting-edge technology solutions in automated science and engineering. The PCL Test Bed will consist of a set of Programmable Cloud Laboratory Nodes (PCL Nodes) that can be remotely accessed to run custom workflows specified and programmed by users, that are linked together via computational networking, shared science questions, and data and artificial intelligence (AI) standards.

The PCL Test Bed will facilitate access to advanced scientific equipment, accelerate translation and scaling of basic research into industry applications, enhance reproducibility and the exchange of experimental data, and assist in training the next generation of scientists and engineers in state-of-the art methodologies. It will help develop community norms, best practices, and formal standards for automated laboratory procedures, workflows, and instrument testing and validation. It will also advance consistent practices for the collection, sharing, and use of metadata and training data and the use and exploitation of AI methods. This program will also support the development of automated laboratory methods, including self-driving autonomous experiment workflows.

Proposals must have a set of well-defined science drivers poised to derive significant benefit from targeted use of the PCL Test Bed capabilities, including but not limited to synthesis, optimization, and/or characterization experiments, in specific sub-disciplines within materials science, biotechnology, chemistry or other areas of science and engineering. These science drivers will guide the protocols and standards necessary for each node and facilitate collaboration across the Test Bed. For example, science drivers could include but are not limited to:

• Materials science, materials synthesis and characterization efforts that advance U.S. competitiveness.
• Biotechnology experiments in scalable, high-throughput engineering and characterization services for proteins or microbes with novel applications in the U.S. bioeconomy.
• High-throughput experimentation for the accelerated development of catalysts to support more efficient chemical synthesis to address urgent national needs.

For the current solicitation, only organizations with pre-existing instrument facilities are eligible to apply to this program to be a PCL Node.

See the solicitation for complete details.

Deadlines

• CU InternalÌýDeadline: 11:59pm MST September 29, 2025
• Sponsor Deadline: 5:00pm MST November 20, 2025

Internal Application Requirements (all in PDF format)

• Project Summary (3 pages maximum): Please include details on the following: 1)Science drivers; 2) Node capabilities; 3) Data & AI capabilities and expertise; 4) Cross-Node collaborations; 5) Training & education plan; 6) User access and outreach strategy; 7) Management plan & timeline; and 8) Post-award sustainability plan. Reference the full NSF solicitation and the internal call’s review criteria for additional details.
• Curriculum Vitae of the Lead PI
• Budget Overview (1 page maximum): A basic budget outlining project costs is sufficient; detailed OCG budgets are not required.

To access the online application, visit:

Eligibility

An individual may serve as PI, co-PI, or Senior Personnel only on one proposal submitted in response to this solicitation.Proposals must include at least one Co-PI with relevant expertise in data management and AI to support the activities described in the proposal.

Only existing shared instrument facilities, or labs of similar capabilities, may submit a proposal to this program. The current solicitation does not support ab initio creation of new lab facilities. Plans for expansion or modification of existing shared instrument facilities can be included with justification for how this will advance the use of the PCL Nodes for the benefit of the proposed science drivers and the PCL Test Bed.

Limited Submission Guidelines

One submission is allowed per organization as lead institution.

Award Information

• Award Amount: $20M per PCL Node ($5M per year over 4 years)
• Anticipated Number of Awards: 4-6

Review Criteria

In addition to the standard NSF Intellectual Merit and Broader Impacts Criteria, reviewers will be asked to consider the following:

• Science Drivers: Clarity, specificity, and potential impact of selected science drivers (e.g., in materials, biotech, catalysts). Are they tightly linked to proposed experiments and the Test Bed’s core goals?
• Node Capabilities: Strength and uniqueness of instruments and infrastructure. How well do these support the proposed science drivers? Quality of the Instrument Inventory Table and justification for any enhancements.
• Data & AI Capabilities: Demonstrated expertise in data management, metadata standards, AI/ML integration, and automation. Presence of a qualified Data & AI lead.
• Cross‑Node Collaboration: Commitment and concrete plans to collaborate on shared standards, workflows, metadata, AI tools, and data sharing across PCL Nodes.
• Training, Outreach & User Access: Realistic and inclusive plans for user recruitment, onboarding workshops, training first-time and advanced users, and broadening access to under-resourced institutions.
• Management Plan & Timeline: Clear roles, governance, and a phased timeline (e.g., alpha, beta, user-ready phases, culminating in post-award sustainability).
• Post‑Award Sustainability: Thoughtful, viable strategies for sustaining operations beyond NSF funding.