Energetic Materials and Manufacturing Initiative (EMMI)
Advancing Research and Development for Mission Success
The Texas A&M University System’s Nuclear Security Office (NSO) has established the Energetic Materials and Manufacturing Initiative (EMMI), which seeks to deepen strategic partnerships between the Nuclear Security Enterprise (NSE) and academic institutions. This initiative will help the NSE ensure that energetic materials – such as conventional high explosives (CHE) and insensitive high explosives (IHE) – are produced with the consistency and quality required to sustain the national nuclear deterrence mission.
The EMMI was kicked off in March 2026 with a High Explosives & Energetics (HE&E) Workshop, where over 50 university faculty, national laboratory scientists, and production plant engineers met to discuss the research needs and technical opportunities related to Design Agencies (DAs) and Production Agencies (PAs) work with energetic materials. The EMMI continues with this Notice of Funding Opportunity (NOFO).
Focus Areas from HE&E Workshop
During the HE&E Workshop, participants discussed workforce development, testing and characterization, materials, manufacturing, modeling and simulation, and perspectives on needs and opportunities relating to energetic materials. Participants concluded that expanded academic partnerships in the focus areas listed below have the potential for significant impact on the NSE mission.
Key Technical Priorities
- Advanced Manufacturing- Innovations in high-consequence, low-volume manufacturing with granular material systems; polymer-particle interactions; and granular material specifications, including innovations in testing and characterization.
- Manufacturing Processes and Infrastructure- Innovations in agile, adaptive manufacturing facilities, including innovations for in-process qualification of product.
- PSPP and Scalability- Discoveries in Process-Structure-Property-Performance (PSPP) relationships; PSPP in the context of process scalability.
- AI/ML Integration- Visionary approaches that establish artificial intelligence/machine learning (AI/ML) frameworks in modern manufacturing; approaches for complex predictions involving sparse training data aimed at material and process qualification.
