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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, AND THE UNIVERSITY OF TEXAS AT AUSTIN ? THE PROJECT ENSURES ACCESS TO A BROAD RANGE OF EXPERTISE AND STATE-OF-THE-ART FACILITIES. THE CORE INTELLECTUAL MERIT LIES IN DEVELOPING AND VALIDATING A SEAMLESS SYSTEM THAT INCORPORATES ROBOT-SUPPORTED, AUTONOMOUS MATERIAL SYNTHESIS AND CHARACTERIZATION INFRASTRUCTURE AND DIGITAL TWINS, ALL TIED TOGETHER BY A SHARED-DATA-INFRASTRUCTURE AND A SELF-IMPROVING AI AGENT. THE AI AGENT INCLUDES THREE SYNERGISTIC CLASSES OF AI TOOLS: THE SKILLS TOOLKIT, THE SEGMENTED LATENT PLANNER, AND THE TRACE-GOVERNED OPTIMIZER ? THAT WILL ENABLE THE AGENT TO REASON, PLAN, AND ADAPT WITHIN THE COMPLEX CONSTRAINTS OF REAL-WORLD MATERIALS SYNTHESIS LABORATORIES. THE CREATION OF DIGITAL TWINS THAT DYNAMICALLY ANTICIPATE OUTCOMES AND HELP THE AI AGENT DESIGN EFFICIENT, SAFE EXPERIMENTAL PROGRAMS REPRESENTS A SIGNIFICANT CONTRIBUTION TO CYBER-PHYSICAL MATERIALS SCIENCE. FURTHERMORE, APPLYING THIS AUTONOMOUS FRAMEWORK TO SYNTHESIZE CHALLENGING NEXT-GENERATION MATERIALS, SUCH AS 2D MATERIALS, COMPLEX OXIDES, AND DIAMOND THIN FILMS, WILL YIELD NEW INSIGHTS INTO THEIR FORMATION MECHANISMS AND PHASE SPACES, DIRECTLY ACCELERATING THE REALIZATION OF MATERIALS PERFORMANCES WITH TARGETED EQM PROPERTIES. THE PCL NODE WILL ALSO ADDRESS CRITICAL CHALLENGES IN REPRODUCIBILITY AND TRANSFERABILITY BY CREATING INTEROPERABLE DATA STANDARDS AND INTEGRATING LABORATORY-SCALE DISCOVERY WITH PILOT-SCALE MANUFACTURING ENVIRONMENTS. EXPECTED OUTCOMES INCLUDE ACCELERATED DEVELOPMENT OF NEXT-GENERATION EQMS WITH TARGETED FUNCTIONALITIES, NEW METHODOLOGIES FOR AUTONOMOUS SCIENTIFIC REASONING, AND BROADLY ACCESSIBLE DATABASES, SOFTWARE, AND WORKFLOWS FOR THE MATERIALS RESEARCH AND DEVELOPMENT COMMUNITY. 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.- SUBAWARDS ARE PLANNED FOR THIS AWARD.

Cooperative Agreement (B) · National Science Foundation · to William Marsh Rice University (UEI K51LECU1G8N3) · data as of 2026-09-30

Source: USAspending.gov prime award transactions and FSRS subaward reports, as loaded by SpendQuery (data as of 2026-09-30). Amounts are obligations (money committed), not outlays. The official record on USAspending.gov ↗