PCL-TEST BED: REVOLUTIONIZING AI-DRIVEN AUTONOMOUS EXPERIMENTATION FOR NEXT-GENERATION SEMICONDUCTOR SYNTHE…

Assistance award details

Assistance award identifier

ASST_NON_2607553_049

FAIN

2607553

Recipient

WILLIAM MARSH RICE UNIVERSITY

Awarding agency

National Science Foundation

Funding agency

National Science Foundation

Assistance listing (CFDA)

47.084

Total obligated

$4,977,740

Period

Aug 1, 2026 – Jul 31, 2030

Fiscal year

2026

Place of performance state

TX

Source file

FY2026_All_Assistance_Full_20261006.zip

Description

PCL-TEST BED: REVOLUTIONIZING AI-DRIVEN AUTONOMOUS EXPERIMENTATION FOR NEXT-GENERATION SEMICONDUCTOR SYNTHESIS (READINESS) -PCL-TEST BED: REVOLUTIONIZING AI-DRIVEN AUTONOMOUS EXPERIMENTATION FOR NEXT-GENERATION SEMICONDUCTOR SYNTHESIS (READINESS) ADVANCED ELECTRONIC AND QUANTUM MATERIALS (EQMS) ARE ESSENTIAL FOR MANY TRANSFORMATIVE TECHNOLOGIES RANGING FROM ARTIFICIAL INTELLIGENCE (AI) AND HIGH-PERFORMANCE COMPUTING TO ENERGY SYSTEMS AND NATIONAL SECURITY APPLICATIONS. SYNTHESIZING, MANUFACTURING, AND OPTIMIZING THESE MATERIALS, HOWEVER, REMAINS SLOW, COSTLY, AND HEAVILY DEPENDENT ON TRIAL-AND-ERROR EXPERIMENTATION, WHICH CONTRIBUTES TO INDUSTRY?S RELUCTANCE TO RAPIDLY ADOPT NEW MATERIALS SYSTEMS THAT COULD OFFER DRAMATIC PERFORMANCE ADVANTAGES. THIS PROJECT DEVELOPS A NEW AI-DRIVEN AUTONOMOUS MATERIALS SYNTHESIS PLATFORM THAT COMBINES ROBOTICS, SIMULATIONS, AND AUTOMATED EXPERIMENTS COORDINATED BY AI AGENTS TO DRAMATICALLY ACCELERATE THE REPRODUCIBLE PRODUCTION OF ADVANCED MATERIALS. THE PLATFORM WILL FUNCTION AS A ?SELF-DRIVING LABORATORY? CAPABLE OF AUTONOMOUSLY DESIGNING, TESTING, AND IMPROVING MATERIAL-SYNTHESIS PROCESSES WITH MINIMAL HUMAN INTERVENTION. BY INTEGRATING DIGITAL AND PHYSICAL WORKFLOWS, THE PROJECT WILL SHORTEN THE TIME REQUIRED TO MOVE FROM SCIENTIFIC DISCOVERY TO SCALABLE MANUFACTURING FOR NEXT-GENERATION EQMS. BEYOND ADVANCING SCIENTIFIC INNOVATION, THE PROJECT WILL STRENGTHEN U.S. LEADERSHIP IN SEMICONDUCTOR AND ADVANCED MANUFACTURING TECHNOLOGIES, SUPPORT WORKFORCE TRAINING IN AI-ENABLED MATERIALS SCIENCE RESEARCH AND DEVELOPMENT AND CREATE REMOTELY ACCESSIBLE TOOLS THAT WILL BROADLY BENEFIT RESEARCHERS, STUDENTS, TECHNOLOGISTS, AND INDUSTRY PARTNERS NATIONWIDE. THIS NSF PROGRAMMABLE CLOUD LABORATORY (PCL) NODE WILL ESTABLISH A CLOSED-LOOP, AUTONOMOUS AND CLOUD-BASED PLATFORM TO ENABLE RAPID, SCALABLE MANUFACTURING OF NEXT-GENERATION EQMS. BY LEVERAGING THE COMPLEMENTARY STRENGTHS OF THE COLLABORATING INSTITUTIONS ? RICE UNIVERSITY, SUNY POLY