The proposed topic and focus area for the proposal must be
clearly stated in the limited submission packet
Phased Program Structure
Projects funded under this solicitation are expected to
propose an approach or cluster of related approaches that will be pursued in
two phases:
Phase I: In the initial phase, teams will design and
demonstrate a clear, tangible research workflow that incorporates AI with
concrete evaluation of the potential for AI advantage. Success may include
demonstrating increased predictive power or scientific insight from
appropriately-curated data, more tightly coupling data and experiments to
validate hypotheses, building new models and analyzing their impact on
discovery speedup, identifying scaling metrics that show how performance
improves with more data or computing resources, improving and speeding up
experimental workflows (e.g., through automation or AI-informed parameters), or
other proposed metrics that the team would like to be considered. The goal is
to provide quantitative analysis of whether a proposed approach is on a
trajectory toward a transformative scientific capability, justifying further
investment.
Phase II: During the second phase, meritorious Phase I and
new Phase II teams will pursue the promising directions identified during the
first phase. DOE envisions a level of effort (including team size and budget)
at 3 to 5 times the initial phase. Receipt of a Phase I award will not be a
prerequisite for submitting a letter of interest and application for Phase II.
If a team believes they have already achieved the goals of Phase I awards, they
may apply directly for a Phase II award in FY26. However, it is anticipated
that most FY26 awards will be Phase I. An amended RFA will be issued to provide
updated instructions about the Phase II LOI and application.
LIMITATIONS ON INSTITUTIONS
Applicant institutions are limited to no more than one
application as the lead institution per focus area for Phase I and Phase II
applications combined. Phase II applications must list a primary focus area but
will have the option to list secondary focus areas. The primary focus area will
be used for determining limitations on institutional submissions.
Cost Sharing
Applicants are expected to follow through on estimated cost
share commitments proposed in their applications if selected for award
negotiations. Unless otherwise specified for the topic, cost sharing is not
required for basic and applied research awarded under this RFA, except
for-profit entities.
Other Eligibility Requirements
In Phase I, applicants must propose small teams with partner
institutions from at least two of the following categories: (1) DOE/NNSA
National Laboratory or a Scientific User Facility5, (2) Industry, and (3)
Institute of Higher Education (IHE)/Non-profit/Other. In Phase II, applicants
will be expected to propose large teams with at least one partner institution
from categories (1) and (2). Inclusion of lead or partner institutions from
category (3) are strongly encouraged but not required. To meet this requirement,
partners must provide intellectual contributions to the proposed project but do
not need to be funded by DOE. Anticipated award amounts: Phase I: $500,000 to $750,000 Phase II: Envisioned as 3 to 5 times the Phase I award. Expected project period: Phase I: 9 months; Phase II: 3 years.
Topics and Focus Areas
Each applicant must address a topic and focus area given
below. Phase I applications are limited to a single focus area. Phase II
applications must identify a primary focus area but can also address secondary
focus areas. Cost share requirements are specific to each focus areas.
1 - Reenvisioning Advanced Manufacturing and Industrial
Productivity
Focus Areas for FY 2026:
A. Agentic AI-Driven Chemical Manufacturing (BES)
B. AI-Driven Materials Processing (BES)
C. AI-Enabled Manufacturing for Extreme Energy Systems (FES)
D. Digitalization of Industrial Processes (ITO)
E. AI-Enabled Smart Manufacturing (AMMTO)
F. Energy Material Manufacturing (AFFO)
2 - Scaling the Biotechnology Revolution
Focus Areas for FY 2026:
A. Biomolecular Science (BER)
B. Genotype to Phenotype (BER)
C. Predictive Engineering of Microbial Communities (BER)
D. Bio Design (BER)
E. AI-Enabled Biological Reaction Engineering, Bioreactor
Design, Process Scale-up and Integration (AFFO)
3 – Securing America’s Critical Minerals Supply
Focus Areas for FY 2026:
A. Resource Mapping and Development (AMMPTO)
B. AI-Enabled Materials Discovery and Engineering (AMMTO)
C. Economic Modeling and Market Analysis (ASO)
D. Extraction and Processing Technologies (AMMPTO, AMMTO)
E. Geological Finders/Keepers (BES, BER)
F. Connections for Isolation (BES)
G. Biological Pathways to CMM (BER)
4 - Delivering Nuclear Energy that is Faster, Safer, Cheaper
Focus Areas for FY 2026: A. Accelerated Nuclear Power Plant Design and Licensing:
Create an automated process to enable rapid design, including safe and secure
autonomous monitoring and control of plant operations, licensing
considerations, and rapid deployment of advanced nuclear technologies using AI.
B. Autonomous Power Plant Operations: Develop AI digital
twin systems that interpret plant operational data in real time, detect
anomalies, and recommend preemptive actions to maintain safety and operational
performance.
C. AI-Assisted Manufacturing and Construction: Support site
selection, born certified manufacturing, construction, supply chain
reliability, and factory modular production methods with AI technologies.
D. Autonomous Research and Development: Condense nuclear
material research and qualification timeframes using AI-driven pipelines for
modeling, characterization, evaluation, and qualification, while integrating
decades of global historical irradiation data.
E. Accelerated Fuel Cycle Facility Design and Licensing to
Secure the Domestic Fuel Supply: Create automated processes to enable rapid
design, licensing considerations, and accelerated deployment of advanced fuel
cycle technologies using AI.
F. AI-Assisted Site Characterization: Accelerate waste
disposition site characterization through AI Modeling.
G. AI-Assisted End Disposition Design: Concept Design for
Disposal of Used Nuclear Fuel and Reprocessed Fuel Waste Streams.
H. Development, Utilization and/or Adoption of AI and ML Tools
to Support the Efficient Review, Classification and Release of Legacy Documents
to the Nuclear Industry.
5 - Accelerating Delivery of Fusion Energy
Focus Areas for FY 2026:
A. Structural Materials (FES)
B. Plasma-Facing Materials (FES
C. Advancing Confinement Approaches (FES)
D. Fuel Cycle and Tritium Processing (FES, NE) E. Tritium Breeding Blankets (FES, NE)
F. Fusion Plant Engineering and System Integration (FES)
G. Plasma Science and Technology (FES)
6 - Transforming Nuclear Restoration and Revitalization
Focus Areas for FY 2026:
A. EM AI R&D Roadmap Implementation (EM-3.2, ASCR, LM)
B. Scale-Bridging AI Foundation Model (EM-3.2, ASCR)
C. Treatment Process Optimization (EM-3.2, ASCR)
7 - Discovering Quantum Algorithms with AI
Focus Areas for FY 2026:
A. Application-aware Error Correction (ASCR)
B. Computational Tools for Fault Tolerant Quantum
Computational Science (ASCR)
C. Hybrid Quantum-Classical Optimization Algorithms (BES)
D. Quantum Algorithms for Nonlinear Plasma Physics (FES)
E. Quantum Advantage for Nuclear and Hadronic Systems (NP,
HEP)
8 - Realizing Quantum Systems for Discovery
Focus Areas for FY 2026:
A. AI for Quantum Systems Design (BES)
B. AI for Control of Quantum System (HEP, NP)
C. AI for Quantum Imaging and Sensing (HEP, NP)
D. AI for Quantum Computing and Networking (ASCR)
9 - Recentering Microelectronics in America
Focus Areas for FY 2026:
A. Angstrom (sub-1-nm) Scale Microelectronics Manufacturing
(AMMTO)
B. Materials and Architectures for Non-von Neuman Computing
Devices (BES) C. AI-Driven Architecture Design (ASCR)
D. 3D non-volatile compute-in-memory technology (ASCR)
E. Physics-Based Circuit Design, Simulation, and Emulation
(ASCR)
F. Microelectronics in Harsh Environments (HEP)
G. Plasma-Enabled Microelectronics Manufacturing (FES)
H. Power Electronics and Communication Networks (ASCR)
I. Low-temperature Electronics for Sensors and Computation
(ASCR, HEP)
J. Transform Neuromorphic Computing Connectivity,
Communication, and System Hardware Integration (ASCR)
10 - Securing U.S. Leadership in Data Centers
Focus Areas for FY26 and 27
A. Data Center Load Flexibility (ITO)
B. Data Center Thermal Management (ITO)
11 - Achieving AI-Driven Autonomous Laboratories
Focus Areas for FY 2026:
A. Advanced Robotics for Dynamic Laboratory Environments
(ASCR)
B. AIOps - AI for Network Operations (ASCR)
C. AI-Accelerated Science: Correlation to Understanding
(BES)
D. AI-Enabled Diagnostics and Remote Handling (FES)
E. Accelerate the design and prototyping of neuromorphic
computing circuit primitives for robotic embodied physical artificial
intelligence (ASCR)
12 - Designing Materials with Predictable Functionality
Focus Areas for FY 2026:
A. Functional to Quantum Materials (BES)
B. Structural Materials (BES, FES, AMMTO)
C. Biomolecular Materials (BES)
D. Plasma-Facing Materials (FES)
E. Targetry by Design (IRP)
F. AI-Enabled Materials Discovery, Development, and
Qualification (AMMTO)
G. Electrochemical Energy Conversion Catalyst Discovery and
Scale up (AFFO)
13 - Enhancing Particle Accelerators for Discovery
Focus Areas for FY 2026:
A. AI-driven Accelerator Facilities (BES, HEP, IRP, NP)
B. Integration of Digital Twins for Fusion Systems and
Actuators (FES)
14 - Unifying Physics from Quarks to the Cosmos
Focus Areas for FY 2026:
A. Foundation Models of Particle Interactions and Cosmic
Physics (HEP, NP):
B. AI Accelerated DUNE Science (HEP)
C. Expedited Discovery from High Complexity and
Petabyte-Scale Datasets (HEP, NP)
15 - Predicting U.S. Water for Energy
Focus Areas for FY 2026:
A. Cloud Microphysics and Atmospheric Turbulence (BER, IESO)
B. Water and Energy (BER)
C. Weeks to Years Prediction (BER)
16 - Scaling the Grid to Power the American Economy
Focus Areas for FY 2026:
A. Grid Modeling and Analysis (OE, CMEI-IESO, SC-ASCR)
B. Grid Operations Optimization (OE, CMEI-IESO, SC-ASCR)
C. Uncertainty Quantification (SC-BER, SC-ASCR, OE,
CMEI-IESO)
17- Unleashing Subsurface Strategic Energy Assets
Focus Areas for FY 2026:
A. Chemical and Hydrologic Transport in Subsurface (BER) B. Evolution of Fractures in the Upper Crust (BES)
C. Control of Subsurface Fractures (HGEO)
18 - HPC Code Curation, Translation, and Development for
Accelerated Scientific Discoveries
Focus Areas for FY 2026:
A. AI-Driven Code Porting and Optimization (ASCR)
B. Automated Scientific Problem-to-Code Generation (ASCR)
C. Neuro-Symbolic Agents for Code Development (ASCR)
D. Performance Prediction and Feedback Loops (ASCR)
E. Trustworthy AI for Scientific Software (ASCR)
F. Multi-Modal Data Integration for Code Intelligence (ASCR)
G. Partnerships for HPC AI Advancement (ASCR, AMMTO)
19 – AI for Scientific Reasoning
Focus Areas for FY 2026:
A. Trustworthy Mathematical and Symbolic Reasoning (ASCR)
B. Hypothesis Generation from Multi-Modal Data (ASCR)
C. Composable and Modular Foundation Models (ASCR)
20 – Cybersecurity for AI-driven Science Workflows
Focus Areas for FY 2026:
A. AI for Adversarial Robustness and Resilience (ASCR)
B. Data Provenance and Integrity Verification (ASCR)
C. Real-Time Attack Detection and Mitigation for AI Models
(ASCR)
21 - Artificial Intelligence in Fluid Flow for Energy
Components and Technologies
Focus Areas for FY 2026:
A. Physics-Informed AI for Complex Flow Modeling (IESO, BER,
ASCR, FES) B. AI-Driven Design and Control for Performance and
Durability (IESO, ASCR)
C. Data-Driven Operational Intelligence and System
Resilience (IESO)
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