Abstract & Details
Description
Award ID: 2607594
NSF PCL-Test Bed: AI Science Foundry: A PCL Testbed Node to Accelerate Breakthroughs in Biology, Chemistry, and Materials The AI Science Foundry at Carnegie Mellon University will become a node in NSFs Programmable Cloud Lab (PCL) Testbed, expanding remote and on-site user access to one of the nations most advanced and comprehensive AI-guided PCL ecosystems for research on biology, chemistry, and metal alloys. It will integrate over 80 robotically controlled scientific instruments with the latest AI agents and models, open data and high-performance computing resources in a unified cybersecure platform that replaces slow, manual trial-and-error processes with rapid, continuous AI-guided design-test-learn cycles. As a PCL node, the AI Science Foundry will enable a broad community of researchers, students, educators and industry users to develop, validate, and run many different AI-guided autonomous experiments on this platform with unprecedented scale, speed, and reproducibility. This shift will dramatically shorten the time for scientific discovery and translation from decades to years or months, accelerating breakthroughs in medicine, biotechnology, energy, manufacturing, and transportation. It will also strengthen the national workforce through education and training in AI, robotics, and data-driven science, ensuring future U.S. economic competitiveness and national security. This project establishes AI Science Foundry at Carnegie Mellon University as a node in NSFs Programmable Cloud Lab (PCL) Testbed, a national infrastructure for AI-guided autonomous experimentation in biology, chemistry, and metal alloys. It will provide a broad community of users with remote and on-site access to one of the nations largest collection of state-of-the-art, co-located capabilities in the: 1) Biological and Chemical Innovation PCL, a 15,000-sq-ft facility with over 80 major scientific instruments for synthesis, characterization, and analysis, and 2) Metal Alloys Innovation PCL, a 2,300-sq-ft facility with instruments for automated metal powder production, alloying, additive manufacturing, and characterization. The modular, open-source Accelerating Science Autonomous Platform (ASAP) integrates these instruments with the latest AI agents and models, robotics, imaging and sensing systems, open data and high-performance computing resources. It will be used to actively search massive multi-modal data and optimize multiparameter spaces, design and orchestrate AI-guided programmable workflows in parallel and collect the resulting data and metadata with unprecedented scale, speed, and reproducibility. In this project, researchers, students, educators, and industry users will develop, validate, and remotely operate AI-guided closed-loop workflows to dramatically shorten the time for research and translation in critical technology areas from decades to years or months. Initial targets include functional polymers for data centers and robotics; microbial biomaterials for sustainable pigments; organoids for personalized medicine; and high-temperature aluminum alloys for lightweight aircraft engines. It will also deliver new user workshops, in-depth, hands-on user instruction, and AI-informed and guided programs to scale training for high school students to industry employees. This will accelerate commercialization of innovations in medicine, biotechnology, energy, manufacturing, and transportation and strengthen the national workforce in AI, robotics, and data-driven science, ensuring future U.S. economic competitiveness and national security. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Waleed Nasser
NSF PCL-Test Bed: AI Science Foundry: A PCL Testbed Node to Accelerate Breakthroughs in Biology, Chemistry, and Materials The AI Science Foundry at Carnegie Mellon University will become a node in NSFs Programmable Cloud Lab (PCL) Testbed, expanding remote and on-site user access to one of the nations most advanced and comprehensive AI-guided PCL ecosystems for research on biology, chemistry, and metal alloys. It will integrate over 80 robotically controlled scientific instruments with the latest AI agents and models, open data and high-performance computing resources in a unified cybersecure platform that replaces slow, manual trial-and-error processes with rapid, continuous AI-guided design-test-learn cycles. As a PCL node, the AI Science Foundry will enable a broad community of researchers, students, educators and industry users to develop, validate, and run many different AI-guided autonomous experiments on this platform with unprecedented scale, speed, and reproducibility. This shift will dramatically shorten the time for scientific discovery and translation from decades to years or months, accelerating breakthroughs in medicine, biotechnology, energy, manufacturing, and transportation. It will also strengthen the national workforce through education and training in AI, robotics, and data-driven science, ensuring future U.S. economic competitiveness and national security. This project establishes AI Science Foundry at Carnegie Mellon University as a node in NSFs Programmable Cloud Lab (PCL) Testbed, a national infrastructure for AI-guided autonomous experimentation in biology, chemistry, and metal alloys. It will provide a broad community of users with remote and on-site access to one of the nations largest collection of state-of-the-art, co-located capabilities in the: 1) Biological and Chemical Innovation PCL, a 15,000-sq-ft facility with over 80 major scientific instruments for synthesis, characterization, and analysis, and 2) Metal Alloys Innovation PCL, a 2,300-sq-ft facility with instruments for automated metal powder production, alloying, additive manufacturing, and characterization. The modular, open-source Accelerating Science Autonomous Platform (ASAP) integrates these instruments with the latest AI agents and models, robotics, imaging and sensing systems, open data and high-performance computing resources. It will be used to actively search massive multi-modal data and optimize multiparameter spaces, design and orchestrate AI-guided programmable workflows in parallel and collect the resulting data and metadata with unprecedented scale, speed, and reproducibility. In this project, researchers, students, educators, and industry users will develop, validate, and remotely operate AI-guided closed-loop workflows to dramatically shorten the time for research and translation in critical technology areas from decades to years or months. Initial targets include functional polymers for data centers and robotics; microbial biomaterials for sustainable pigments; organoids for personalized medicine; and high-temperature aluminum alloys for lightweight aircraft engines. It will also deliver new user workshops, in-depth, hands-on user instruction, and AI-informed and guided programs to scale training for high school students to industry employees. This will accelerate commercialization of innovations in medicine, biotechnology, energy, manufacturing, and transportation and strengthen the national workforce in AI, robotics, and data-driven science, ensuring future U.S. economic competitiveness and national security. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Waleed Nasser
| Status | Active |
|---|---|
| Effective start/end date | 08/01/26 → 07/31/30 |
Lead and Sub-Awardee Organization(s)
Funding
- (PCL Test Bed) Toward a Network of Programmable Cloud Laboratories: $20,000,000.00
Active Fiscal Year
- FY2028
- FY2027
- FY2026
- FY2029
- FY2030
Start Fiscal Year
- FY2026
TIP Programs
- (PCL Test Bed) Toward a Network of Programmable Cloud Laboratories
Key Technology Areas
- Artificial Intelligence
- (confidence score: 88%)
- Data and Cybersecurity
- (confidence score: 81%)
- Advanced Computing and Semiconductors
- (confidence score: 90%)
- Robotics and Advanced Manufacturing
- (confidence score: 100%)
Technology Foci
- Automation
- (confidence score: 94%)
- Advanced Computing and Semiconductors (Broad)
- (confidence score: 100%)
- Data and Cybersecurity (Broad)
- (confidence score: 100%)
- Machine Learning Training Data
- (confidence score: 92%)
Congressional District at Award
- District n. 12 of Pennsylvania
Current Congressional District
- District n. 12 of Pennsylvania
United States
- Pennsylvania
Core Based Statistical Area (CBSA)
- Pittsburgh, PA
County
- County: Allegheny, PA
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