# Project RU: Basic Research for Countering WMD

**R-2A activity** of project RU — BASIC RESEARCH FOR COUNTERING WMD  
**Program element:** 0601000BR — DTRA BASIC Research  
**Component:** Defense-Wide · **Budget Activity:** 1  
**Vintage:** President's Budget PB2027  
**Canonical URL:** https://hitchintel.com/programs/0601000BR/RU/a0  
**Parent:** https://hitchintel.com/programs/0601000BR

## Summary

This activity requests $15.1M in FY2027, 100% of project RU, down 2.6% on FY2026. The R-2A exhibit describes it across FY2025–FY2027, including what the FY2027 money is planned to buy.

## What the FY2027 request buys

**FY2027 planned work.** - Advance knowledge of how materials behave and chemistries evolve within extreme WMD environments. - Implement machine learning analysis techniques in hyperspectral imaging, high speed spectroscopy, and in-situ visualization. - Increase understanding of material properties and radiation interactions to continue transformative improvements in energy resolution using low-cost solids with high structural flexibility. - Develop Artificial Intelligence-based predictions of material behavior for use in follow-on RDT&E. - Transition models, simulations, materials, and analysis techniques to applied research partners. - Transition students, postdocs, and researchers into critical roles within the Department of War and Department of Energy.

**FY2026 to FY2027 change.** The change from FY 2026 to FY 2027 realigns lower priority spending with the Secretary of War’s highest priorities to strengthen readiness, modernize capabilities, and sustain the force. This includes a re-baselining of the Department’s Science and Technology funding and a reduction in travel spending.

## Before the request year

**FY2026 plans — current year.** Maintain two University Research Alliances (1) Materials Science in Extreme Environments - Advance knowledge of how materials behave, and chemistries evolve within extreme WMD environments. - Implement machine learning analysis techniques in hyperspectral imaging, high speed spectroscopy, and in-situ visualization. - Develop Artificial Intelligence-based predictions of material behavior. -Transition models, simulations, materials, and analysis techniques to applied research partners. - Initiate rapid response projects to enable new research areas and transitions. - Transition students, postdoctoral researchers (“post docs”), and scientists into critical roles within the DoW and Department of Energy (DOE). - Expand internship and exchange programs with DoW laboratory partners. - Expand workshop and professional development opportunities for students, postdocs, active duty, and DoW civilians. (2) Interaction of Ionizing Radiation with Matter - Research on the impact of Artificial Intelligence on materials modeling. - Increase understanding of material properties and radiation interactions to continue transformative improvements in energy resolution using low-cost solids with high structural flexibility. -Transition models, simulations, materials, and analysis techniques to applied research. - Initiate rapid response projects to enable new research areas and transitions. - Expand Sea Air and Land Challenge and other Science, Technology, Engineering, and Mathematics outreach initiatives engaging a greater number of schools and participants. - Prepare students, postdocs, and researchers for critical roles within the DoW and DOE.

**FY2025 accomplishments.** - Maintain two University Research Alliances University Research Alliances: Materials Science in Extreme Environments: - Complete or mature foundational research (progress is performer-specific) in the areas of enhanced computational modeling for agent defeat scenarios, and quantification of uncertainty in nuclear blast simulation modeling. - Finalize experimental scaling of ablation of targets using optical lasers and X-rays validated by experiments to measure and predict shock impact from nuclear blasts. Transition machine learning analysis in hyperspectral imaging, high speed spectroscopy, and in-situ visualization. University Research Alliances: Interaction of Ionizing Radiation with Matter: -Complete or mature foundational research (progress is performer-specific) including the development and assessment of low-cost methods for assessing chip vulnerability, and implementation of Artificial Intelligence-driven modeling techniques to develop novel semiconductor systems. -Demonstrate enhanced energy resolution from scintillators through a computationally driven surface engineering of photonic crystal structures. Construct machine learning models that can rapidly identify synthesizable materials which are verifiable by theory, simulation, and experiments.

## Funding

| Fiscal Year | Estimate Type | Amount ($M) |
|---|---|---|
| FY2025 | Actual | 14.9 |
| FY2026 | Enacted | 15.5 |
| FY2027 | Request | 15.1 |

> Prior, current and budget year only — an R-2A activity carries no five-year plan. It sums exactly into its project in the request year and not necessarily in any other.

## Source & machine access

- **Source:** FY2027 Office of the Secretary of Defense RDT&E Budget Justification, Exhibit R-2A, PE 0601000BR project RU (PB PB2027). Narrative is the government's own text.
- **No marks, no contractors at this grain** — congressional marks land on the program element and R-3 performers on the project.
- **MCP:** `mcp.hitchintel.com` — `budget_get_activity`.

*HitchAI is an independent intelligence service, not affiliated with the U.S. Department of Defense. Budget figures are requests/estimates, not obligations.*