What the FY2027 request buys
Verbatim from the R-2A exhibit for project RU of PE 0601000BR. This is the budget justification's own description of work that has not happened yet — the one thing no other level of the budget carries.
- 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.
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.
FY2025–FY2026: what came before
Prior-year accomplishments and current-year plans from the same exhibit. Context for the FY2027 plan, not a series — an activity partitions its project exactly in the request year, but can under-cover it in earlier years.
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.
- 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.
Three years, and no five-year plan
An R-2A activity publishes the prior year, the current year and the budget year. The FYDP outyears exist at project and program-element level and are deliberately absent here rather than inferred. Estimate types are colored and never summed into one figure.
| Fiscal Year | Estimate Type | Amount ($M) |
|---|---|---|
| FY2025 | Actual | 14.9 |
| FY2026 | Enacted | 15.5 |
| FY2027 | Request | 15.1 |
This activity is 100% of project RU's FY2027 request and 100% of PE 0601000BR's. In the request year the activities under a project sum to it exactly; in the current year they under-cover it in about 9% of cases, so an activity's delta can legitimately exceed its parent's and the two must not be compared row to row.
1 activity in project RU
Every R-2A line of this project, largest FY2027 request first. Linked where the activity has enough of its own narrative to carry a page; the rest are shown in full on the program-element page.