AI-Enabled Programmable Cloud Laboratories is sponsored by National Science Foundation (NSF). NSF has announced new funding opportunities to establish a national network of AI-enabled programmable cloud laboratories.

Test Bed: Toward a Network of Programmable Cloud Laboratories (PCL Test Bed) | NSF - U.S. National Science Foundation Test Bed: Toward a Network of Programmable Cloud Laboratories (PCL Test Bed) Important information for proposers and award recipients All proposals must be submitted in accordance with the requirements specified in the funding opportunity and in the Proposal & Award Policies & Procedures Guide (PAPPG) and its supplements .

All NSF grants and cooperative agreements are subject to the applicable set of NSF award terms and conditions . NSF has updated its research security policies for NSF funded projects. Supports the development and operation of a national network of AI-powered, remotely accessible laboratories to accelerate scientific discovery, enhance reproducibility and improve access to advanced research tools in science and engineering.

Supports the development and operation of a national network of AI-powered, remotely accessible laboratories to accelerate scientific discovery, enhance reproducibility and improve access to advanced research tools in science and engineering. 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 are able to 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.

User Recruitment and On-Boarding Workshops will be a key component of the PCL Test Bed program and will serve to recruit users to individual PCL Nodes and the Test Bed to help make progress on the proposed science drivers, provide access to technology, test the limits of the experimental set-up of the nodes, and explore new research opportunities between the PCL Nodes and institutions including, but not limited to, R2 Universities, PUI (Primarily Undergraduate Institutions), and two-year institutions.

The PCL Test Bed will be available to researchers in academia as well as industry, including current and former awardees from the Small Business Innovation Research/Small Business Technology Transfer (SBIR/STTR) programs. The portfolio of projects is available here, https://seedfund. nsf.

gov/portfolio . PCL Nodes are expected to develop and implement plans for continued operation after the period of this award.

Updates and announcements NSF PCL program resources October 16, 2025 - Office Hours and Teaming Opportunity: NSF PCL Test Bed September 26, 2025 - Office Hours and Teaming Opportunity: NSF PCL Test Bed September 5, 2025 - Office Hours and Teaming Opportunity: NSF PCL Test Bed Awards made through this program Browse projects funded by this program Map of recent awards made through this program Directorate for Technology, Innovation and Partnerships (TIP) Directorate for Mathematical and Physical Sciences (MPS)