THIS PROPOSAL SEEKS TO DEMONSTRATE THE APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING METHODS TO AUGMENT MATERIALS DISCOVERY EFFORTS WITHIN CHEMICAL BIOLOGICAL DEFENSES PROTECTION AND HAZARD MITIGATION MISSION SPACE, SPECIFICALLY IN SELF-DETOXIFYING SUITS AND REGENERATIVE SUITS. THIS PROPOSAL SHOULD PROVIDE A NEW MEANS OF IDENTIFYING NOVEL HETEROGENEOUS CATALYST MATERIALS WITH SUPERIOR DEGRADATION PERFORMANCE, MAXIMUM UPTAKE, AND TURNOVER OF ORGANOPHOSPHATE CHEMICALS. THE PROPOSAL WILL DEMONSTRATE ADAPTATION OF AUTONOMOUS EXPERIMENTAL WORKFLOWS TO EXECUTE MATERIALS SYNTHESIS, PROCESSING, AND CHARACTERIZATION ACTIVITIES WITH A FOCUSED EXPLORATION ON STRUCTURE ACTIVITY RELATIONSHIP SPACE. THE DELIVERABLES ARE ANNUAL REPORTS THAT DESCRIBE THE RESEARCH PROGRESS AND FINDINGS, CONTRIBUTING TO THE ADVANCEMENT OF KNOWLEDGE AND FOSTERING FURTHER RESEARCH IN HETEROGENEOUS CATALYSIS. THE EXPECTED OUTCOMES ARE TO ESTABLISH AI ML GUIDED EXPERIMENTATION WORKFLOWS THAT SYNTHESIZE MATERIALS, EXPLORE STRUCTURE-ACTIVITY RELATIONSHIP SPACE, AND VALIDATE HIGH-PERFORMING MATERIALS. THESE OUTCOMES HAVE GREAT POTENTIAL TO INCREASE THE SPEED AND EFFICIENCY FOR COLLECTING CRUCIAL DATA IN BASIC AND EARLY APPLIED RESEARCH & DEVELOPMENT MATERIALS RESEARCH. THIS WOULD SUPPORT TECHNOLOGICAL RISK REDUCTION ACTIVITIES BY THE CHEMICAL AND BIOLOGICAL DEFENSE PROGRAM CBDP SCIENCE AND TECHNOLOGY AND BENEFIT THE ACQUISITION PROGRAM IN NEXT GENERATION INDIVIDUAL PROTECTION.