DE-FOA-0003600.pdf

FY 2026 Continuation of Solicitation for the Office of Science Financial Assistance Program

Notice of Funding Opportunity (NOFO) Number:

DE-FOA-0003600

NOFO Type: Initial

Assistance Listings: 81.049

NOFO Issue Date: September 30, 2025 Submission Deadline for Pre-Applications: A Pre-Application is optional/encouraged.

A Pre-Application may be required for consideration by certain review panels.

Submission Deadline for Applications: This NOFO will remain open until September 30, 2026, or until replaced by a successor NOFO. Applications may be submitted any time during that period.

Individual topics in this NOFO may have scheduled review panels. Applications submitted after the panel’s acceptance date may be held until the next review panel.

Table of Contents

TABLE OF CONTENTS

I. BASIC INFORMATION

EXECUTIVE SUMMARY

FUNDING DETAILS

KEY FACTS

KEY DATES

AGENCY CONTACT INFORMATION

RECOMMENDATION

II. ELIGIBILITY

A. ELIGIBLE APPLICANTS

B. COST SHARING

C. ELIGIBLE INDIVIDUALS

III. PROGRAM DESCRIPTION

A. PURPOSE

1. ADVANCED SCIENTIFIC COMPUTING RESEARCH (ASCR)

2. BASIC ENERGY SCIENCES (BES)

3. BIOLOGICAL AND ENVIRONMENTAL RESEARCH (BER)

4. FUSION ENERGY SCIENCES (FES)

5. HIGH ENERGY PHYSICS (HEP)

6. NUCLEAR PHYSICS (NP)

7. ISOTOPE R&D AND PRODUCTION (IRP)

B. PROGRAM GOALS, OBJECTIVES, AND PRIORITIES

C. AWARD CONTRIBUTION TO GOALS AND OBJECTIVES

D. PERFORMANCE GOALS

E. SUBSTANTIAL INVOLVEMENT

F. PROGRAM UNALLOWABLE COSTS

G. CITATIONS TO STATUTE AND REGULATIONS

H. PROGRAM HISTORY

I. OTHER INFORMATION

IV. APPLICATION CONTENTS AND FORMAT

A. PRELIMINARY SUBMISSIONS

B. APPLICATION

C. COMPONENT PIECES OF THE APPLICATION

D. INFORMATION THAT MUST BE SUBMITTED AFTER APPLICATION BUT

BEFORE AWARD

V. SUBMISSION REQUIREMENTS AND DEADLINES

A. ADDRESS TO REQUEST APPLICATION PACKAGE

B. UNIQUE ENTITY IDENTIFIER (UEI) AND SYSTEM FOR AWARD MANAGEMENT

(SAM.GOV)

C. SUBMISSION INSTRUCTIONS

D. SUBMISSION DATES AND TIMES

VI. APPLICATION REVIEW INFORMATION

A. RESPONSIVENESS REVIEW

B. REVIEW CRITERIA

C. REVIEW AND SELECTION PROCESS

VII. AWARD NOTICES

A. TYPE OF AWARD INSTRUMENT

B. ANTICIPATED TIMELINE FOR NOTICE OF SELECTION FOR AWARD

NEGOTIATION

VIII. POST-AWARD REQUIREMENTS AND ADMINISTRATION

A. ADMINISTRATIVE AND NATIONAL POLICY REQUIREMENTS

B. REPORTING

C. REPORTING OF MATTERS RELATED TO RECIPIENT INTEGRITY AND

PERFORMANCE (DECEMBER 2015)

D. INTERIM CONFLICT OF INTEREST POLICY FOR FINANCIAL ASSISTANCE

IX. OTHER INFORMATION

A. CHECKLIST FOR AVOIDING COMMON ERRORS

B. HOW-TO GUIDES

C. ADMINISTRATIVE AND NATIONAL POLICY REQUIREMENTS

D. REFERENCE MATERIAL

I. Basic Information

U.S. Department of Energy (DOE) Office of Science (SC)

Executive Summary

The Office of Science (SC) of the Department of Energy (DOE) hereby announces its continuing interest in receiving applications for support of work in the following program areas: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, Nuclear Physics, and Isotope R&D and Production. On September 3, 1992, DOE published in the Federal Register the Office of Energy Research Financial Assistance Program (now called the Office of Science Financial Assistance Program), 10 CFR 605, as a Final Rule, which contained a solicitation for this program. Information about submission of applications, eligibility, limitations, evaluation and selection processes and other policies and procedures are specified in 10 CFR 605.

This NOFO is our annual open solicitation that covers all research areas in SC and is open throughout the Fiscal Year. Any research within SC’s Congressionally authorized mission may be proposed under this NOFO.

This NOFO will remain open until September 30, 2026, 11:59 PM Eastern Time, or until it is succeeded by another issuance, whichever occurs first. This NOFO succeeds DE-FOA- 0003432, which was published September 30, 2024.

Funding Details

Expected total available funding Approximately $500,000,000 in current and future fiscal year funds.

Expected number of awards Historically, 200 to 350 new awards have been made in response to the NOFO each year.

Expected dollar amount of individual awards

Historically, awards from $5,000 to $5,000,000 have been made in response to the NOFO each year.

Expected award project period Awards are expected to be made for a project period of six months to five years, with the most common project period being three years in duration.

Key Facts

NOFO Title FY 2026 Continuation of Solicitation for the Office of Science

Financial Assistance Program NOFO Number DE-FOA-0003600 Announcement Type Initial Assistance Listing 81.049 Statutory Authority The programmatic authorizing statute is:

Section 646 of Public Law 95-91, U.S. Department of Energy Organization Act Section 901, et seq. of Public Law 109-58, Energy Policy Act of Section 401 of Public Law 115-368, National Quantum Initiative Act

Governing Regulations

Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards, codified at 2 CFR 200 U.S. Department of Energy Financial Assistance Rules, codified at

2 CFR 910

U.S. Department of Energy, Office of Science Financial Assistance Program Rules, codified at 10 CFR 605 U.S. Department of Energy Other Transaction Agreements Rules, codified at 2 CFR 930

Key Dates

Key dates are printed on the cover of this NOFO.

Agency Contact Information

Grants.gov Customer Support

800-518-4726 (toll-free) support@Grants.gov

PAMS

Customer Support

855-818-1846 (toll-free) 301-903-9610 sc.pams-helpdesk@science.doe.gov

Technical/Scientific Program Contact

Questions regarding the program technical requirements must be directed to the point of contact listed for each program area within this NOFO.

Administrative Contact (questions about budgets and eligibility) sc.opencall@science.doe.gov mailto:support@grants.gov mailto:sc.pams-helpdesk@science.doe.gov mailto:sc.opencall@science.doe.gov

Recommendation

SC encourages you to register in all systems as soon as possible. You are also encouraged to submit letters of intent (LOIs), pre-applications, and applications well before the deadline.

II. Eligibility

A. Eligible Applicants

All types of applicants are eligible to apply, except nonprofit organizations described in section 501(c)(4) of the Internal Revenue Code of 1986 that engaged in lobbying activities after December 31, 1995.

Federally affiliated1 entities must adhere to the eligibility standards below:

1. DOE/NNSA National Laboratories

DOE/NNSA National Laboratories are not eligible to submit applications under this NOFO but may be proposed as subrecipients under another organization’s application. If recommended for funding as a proposed subrecipient, the value of the proposed subaward will be removed from the prime applicant’s award and will be provided to the laboratory through the DOE Field-Work Proposal System and work will be conducted under the laboratory’s contract with DOE. No administrative provisions of this NOFO will apply to the laboratory or any laboratory subcontractor. Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section IX of this NOFO.

2. Non-DOE/NNSA FFRDCs

Non-DOE/NNSA FFRDCs are eligible to submit applications (either as a lead organization or as a team member in a multi-institutional team) under this NOFO and may be proposed as subrecipients under another organization’s application. If recommended for funding as a lead applicant or a team member, funding will be provided through an interagency agreement Award to the FFRDC’s sponsoring Federal Agency. If recommended for funding as a proposed subrecipient, the value of the proposed subaward may be removed from the prime applicant’s award and may be provided through an Inter-Agency Award to the FFRDC’s sponsoring Federal Agency. Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section IX of this NOFO.

3. Other Federal Agencies

Other Federal Agencies are eligible to submit applications (either as a lead organization or as a team member in a multi-institutional team) under this NOFO and may be proposed as subrecipients under another organization’s application. If recommended for funding as a lead applicant or a team member, funding will be provided through an interagency

1 Institutions that are not DOE/NNSA National Laboratories, a non-DOE/NNSA FFRDC, or another Federal agency are not Federally affiliated, even if they receive Federal funds or perform work under a Federal award or contract.

agreement. If recommended for funding as a proposed subrecipient, the value of the proposed subaward may be removed from the prime applicant’s award and may be provided through an interagency agreement. Additional instructions for providing statutory authorization are found in Section IX of this NOFO.

Notes for applicants of all types:

• Individual applicants are unlikely to possess the skills, abilities, and resources to successfully accomplish the objectives of this NOFO. Individual applicants are encouraged to address this concern in their applications and to demonstrate how they will accomplish the objectives of this NOFO.

• Non-domestic applicants are advised that successful applications from non-domestic applicants include a detailed demonstration of how the applicant possesses skills, resources, and abilities that do not exist among potential domestic applicants.

This NOFO does not support an applicant’s commercial activity. This NOFO seeks to support basic research to advance understanding rather than to address commercial opportunities. Applications that propose research related to current commercial activity or current customer needs may be declined without merit review. All for-profit applicants must include a description, not to exceed 200 words of how their proposed work will advance scientific understanding of a basic and fundamental nature as an appendix to the Project Narrative.

B. Cost Sharing

Cost sharing for basic and fundamental research financial assistance awards is not required pursuant to an exclusion from the requirements of Section 988 of the Energy Policy Act of 2005. For technology investment agreements, to the maximum extent practicable, non- Federal parties carrying out a RD&D project under a TIA are to provide at least 50% cost sharing.

Cost sharing is not required of DOE/NNSA National Laboratories, other Federal agencies, another Federal agency’s FFRDC, or their subcontractors at any tier. DOE/NNSA National Laboratories, other Federal agencies, and another Federal agency’s FFRDC may impose cost-sharing requirements on their contractors subject to their policies and procedures.

Cost sharing will not be considered as a factor during merit review or award selection.

C. Eligible Individuals

Individuals with the skills, knowledge, and resources necessary to carry out the proposed research as a Principal Investigator (PI) are invited to work with their organizations to develop an application.

III. Program Description

A. Purpose

The DOE SC program hereby announces its continuing interest in receiving applications for support of work in the following program areas: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, Nuclear Physics, and Isotope R&D and Production. This NOFO is our annual open solicitation that covers all research areas in SC and is open throughout the Fiscal Year. Any research within SC’s Congressionally authorized mission may be proposed under this NOFO.

SUPPLEMENTARY INFORMATION

1. Advanced Scientific Computing Research (ASCR)

(a) Applied Mathematics

(b) Computer Science

(c) Computational Partnerships

(d) Advanced Computing Technologies

2. Basic Energy Sciences (BES)

(a) Materials Chemistry

(b) Biomolecular Materials

(c) Synthesis and Processing Science

(d) Experimental Condensed Matter Physics

(e) Theoretical Condensed Matter Physics

(f) Physical Behavior of Materials

(g) Mechanical Behavior and Radiation Effects

(h) Quantum Information Science in Materials Sciences and Engineering

(i) X-ray Scattering

(j) Neutron Scattering

(k) Electron and Scanning Probe Microscopies

(l) Atomic, Molecular, and Optical Sciences

(m) Gas Phase Chemical Physics

(n) Computational and Theoretical Chemistry

(o) Condensed Phase and Interfacial Molecular Science

(p) Quantum Information Science Research in Chemical Sciences, Geosciences, and

Biosciences

(q) Catalysis Science

(r) Separation Science

(s) Heavy Element Chemistry

(t) Geosciences

(u) Photochemistry and Radiation Chemistry

(v) Photosynthetic Systems

(w) Physical Biosciences

(x) BES Accelerator and Detector Research

3. Biological and Environmental Research (BER)

(a) Microbiome Research

(b) Atmospheric Process Research

(c) Environmental Systems Process Research

(d) Earth-Energy Systems Modeling

4. Fusion Energy Sciences (FES)

(a) Theory & Simulation

(b) Artificial Intelligence and Machine Learning for Fusion & Plasma Science

(c) Fusion Materials and Internal Components

(d) Toroidal Long Pulse

(e) Compact Toroidal Concepts

(f) Inertial Fusion Energy

(g) Measurement Innovation

(h) Closing the Fusion Cycle: Fusion Nuclear Science

(i) Closing the Fusion Cycle: Enabling Research and Development

(j) Plasma Science and Technology – General Plasma Science

(k) Plasma Science and Technology – High Energy Density Physics

(l) Plasma Science and Technology – Microelectronics Research

(m) Plasma Science and Technology – Quantum Information Science

(n) Public-Private Partnerships

5. High Energy Physics (HEP)

(a) Experimental Research at the Energy Frontier in High Energy Physics

(b) Experimental Research at the Intensity Frontier in High Energy Physics

(c) Experimental Research at the Cosmic Frontier in High Energy Physics

(d) Theoretical Research in High Energy Physics

(e) Accelerator Science and Technology R&D in High Energy Physics

(f) Instrumentation and Detector R&D in High Energy Physics

(g) Computational Research in High Energy Physics

(h) Quantum Information Science for High Energy Physics Research

(i) Accelerator Stewardship and Accelerator Development

6. Nuclear Physics (NP)

(a) Medium Energy Nuclear Physics

(b) Heavy Ion Nuclear Physics

(c) Nuclear Structure and Nuclear Astrophysics

(d) Fundamental Symmetries

(e) Nuclear Theory

(f) Nuclear Data

(g) Nuclear Physics Computing

(h) Accelerator Research and Development for NP Facilities

(i) Artificial Intelligence and Machine Learning Applications

(j) Quantum Information Science for Nuclear Physics Research

(k) Generic Detector Research and Development

7. Isotope R&D and Production (IRP)

(a) Targetry and Isotope Production Research

(b) Nuclear and Radiochemical Separation, Purification and Radiochemical Synthesis

(c) Biological Tracers, Imaging, and Therapeutics

1. Advanced Scientific Computing Research (ASCR) Program Website: https://science.osti.gov/ascr

The mission of the Advanced Scientific Computing Research (ASCR) program is to advance applied mathematics and computer science, including artificial intelligence (AI) and quantum information science (QIS); deliver the most sophisticated computational scientific applications in partnership with disciplinary science; create first-of-a-kind advanced computing and networking capabilities for the Nation; and develop future generations of computing hardware and software tools for science and engineering in partnership with the research community, including U.S. industry.

ASCR’s research and facilities investments increase the capability, versatility, and efficiency of scientific computing through activities described by four thrusts:

• Breakthrough Tools and Technologies: ASCR enhances software, data processes, and AI for increasingly complex or resource intense modeling and simulation, including enabling the convergence of AI with QIS.

• Deep Understanding of AI and Physical Models: ASCR advances and enables knowledge in core mathematical methods and algorithms that underlie all AI, modelling, and simulation.

• Enabling High-precision Research and Development: ASCR focuses on concurrently advancing applied math and computer science knowledge with disciplinary science in critical areas such as fusion energy and material science.

• Hardware Innovation: ASCR increases the robustness of computing, including underlying communication and energy needs, redefines the art of possible in conventional computing, and leads the development of new emerging technologies.

ASCR supports cross-disciplinary research in which domains of scientific inquiry may provide problems and data that provide use-cases for computer scientists and applied mathematicians to devise generalized methods, models, algorithms and tools. ASCR’s interest in these fields is not to solve the specific problems in other scientific domains but to use those challenges to advance the state of the art and increase knowledge in its fields of https://science.osti.gov/ascr research. ASCR advances key areas of computational science and discovery that support the missions of SC through mutually beneficial partnerships.

The computing resources and high-speed networks required to meet SC needs exceed the state-of- the-art by a significant margin. Furthermore, the system software, algorithms, software tools and libraries, programming models and the distributed software environments needed to accelerate scientific discovery through modeling and simulation are often beyond the realm of commercial interest. To establish and maintain DOE’s modeling and simulation leadership in scientific areas that are important to its mission, ASCR operates leadership computing facilities, a high-performance production computing center, research prototypes, and a high-speed network, implementing a broad base research portfolio in applied mathematics, computer and network sciences, and computational science to solve complex problems on computational resources at the exascale and beyond.

Further information on ASCR facilities can be found at:

https://science.osti.gov/ascr/Facilities.

For all ASCR subprograms: Submission of preliminary research descriptions (e.g., pre-applications, concept papers) is strongly encouraged. They will be reviewed for responsiveness of the proposed work to the research topics. You should send an email to a subprogram contact for information regarding format and content.

The ASCR subprograms and their objectives follow:

(a) Applied Mathematics

This subprogram supports basic research leading to fundamental mathematical advances and computational breakthroughs across DOE and SC missions. Important areas of basic research include: (1) novel deterministic or randomized numerical methods for the scalable solution of large-scale, linear and nonlinear systems of equations, including those solution methods that take into consideration the possibilities brought about by future high performance computing (HPC) architectures; (2) optimization techniques and next-generation solvers; (3) numerical methods for modeling multiscale, multi-physics, or multi-component continuous or discrete systems that span a wide range of time and length scales;

(4) methods of simulation and analysis of systems that account for the uncertainties of the systems, or are inherently stochastic or uncertain; (5) innovative approaches for analyzing, extracting insight from, or reducing large-scale data sets; and (6) foundational research in scientific machine learning and artificial intelligence (AI) as a cross-cutting area of interest for enabling greater adaptivity, automation, and predictive capabilities in scientific computing.

Areas that are out of scope include:

• Topics not covered in the list of Applied Mathematics topics above, except with the specific encouragement of an Applied Mathematics program manager in response to an https://science.osti.gov/ascr/Facilities emailed concept paper;

• Research and applications not motivated and justified in the context of current and future SC user facilities, especially those supported by ASCR (i.e., Argonne Leadership Computing Facility [ALCF], Oak Ridge Leadership Computing Facility [OLCF], and National Energy Research Scientific Computing Center [NERSC]):

https://science.osti.gov/ascr/Facilities;

• Application-specific research. The Applied Mathematics program seeks research focused on innovative and novel mathematics, not on existing mathematical techniques applied to new applications. Innovative and novel mathematics appropriate for ASCR are typically generalizable to multiple applications, and successful applications often demonstrate such generalizability in the context of two or more applications; and

• Approaches that are not efficient and scalable for problems of increasingly high dimensionality and computational complexity and that do not take advantage of current and emerging high-performance computing architectures or ecosystems.

Notice of Submission Requirements for the Panel Review on the topic of Inverse Methods for Complex Systems. ASCR held a basic-research-needs workshop on “Inverse Methods for Complex Systems,” June 10-12, 2025, with the aim of identifying research priorities in developing new algorithms and methods for solving inverse problems within the mission space of the Department of Energy. For more information, see https://www.orau.gov/InverseMethods. ASCR expects to convene a merit-review panel in March 2026 for applications submitted in this area. Topics included discovery, utilization, and preservation of structure, model limitations, multimodal data, goal-oriented inverse problems, and scalable algorithms. Applicants are encouraged to consider the challenges associated with massive and high-dimensional data. Successful applications will advance knowledge in applied mathematics and will apply to multiple scientific domains. To be considered by the panel, a pre-application must be submitted by November 14, 2025. A pre-application may involve researchers from a single or multiple eligible institutions. Each pre-application will be reviewed for responsiveness and competitiveness of the proposed research. ASCR expects to provide pre-application encouragement and discouragement decisions by December 15, 2025. To be reviewed by the panel, an application must be associated with an encouraged pre-application, submitted by January 16, 2026, and request no more than $1,000,000 per year in total across all institutions for a three-year award.

ASCR expects to make at most three awards.

Subprogram Contact:

• David Rabson, david.rabson@science.doe.gov Website: https://science.osti.gov/ascr/Research/Applied-Mathematics

(b) Computer Science

The Computer Science research program supports research that enables computing and networking at extreme scales and the understanding of extreme scale, or complex data from https://science.osti.gov/ascr/Facilities https://www.orau.gov/InverseMethods mailto:david.rabson@science.doe.gov https://science.osti.gov/ascr/Research/Applied-Mathematics both simulations and experiments. It aims to make high performance scientific computers and networks highly productive and efficient to solve scientific challenges while attempting to reduce domain science application complexity as much as possible. The computer science program does this in the context of sharp increases in the heterogeneity and complexity of computing systems; the need to integrate simulation, data analysis, and other tasks seamlessly and intelligently into coherent and usable workflows; and the challenges posed by highly novel computing platforms, such as neuromorphic and quantum systems.

Priority interests for the program include the following. Applications are not restricted to a single topic and may span several topics.

• Artificial intelligence (AI) for science:

ASCR supports the development of parallel, distributed, or federated algorithms and new computational approaches for scientific AI. AI research under the Computer Science topic may focus on optimization and scalability, especially in the context of next-generation computing platforms for science, and the integration needed for enabling AI-driven science and engineering workflows. Foundational AI research under the Applied Mathematics topic (see section (a) above) investigates new modes of scientific machine learning.

• Data analysis and visualization:

SC-supported researchers and facilities are generating large, complex, multi-modal data at unprecedented rates. There is a need for advanced visualizations and visual analytics tools for making sense of these data and making operational decisions. This program solicits research to develop techniques for deriving and visualizing insights from large scale and/or complex simulation, experimental, or observational data or combinations of these as relevant to SC and DOE priority applications: visual analysis of high-dimensional data at scale, data from multiple sources and of varying types, attributes such as uncertainty, and data in the context of domain-specific knowledge; and visual analytic approaches to understanding artificial intelligence/machine learning outcomes or the state and behavior of a supercomputing system at scale. Also of interest are machine learning or AI techniques for data analysis that are scalable, energy-efficient, explainable, or involve knowledge extraction. Possible topics are highlighted in the “Report for the ASCR Workshop on Visualization for Scientific Discovery, Decision- Making, and Communication”, https://doi.org/10.2172/1845709.

• Continuum Computing:

Scientific computing will increasingly incorporate a number of different tasks that need to be managed along with the main simulation or experimental tasks—for example, ensemble analysis, data-driven science, artificial intelligence, machine learning, surrogate modeling, and graph analytics. Many of these tasks will need to be executed concurrently with simulations and experiments sharing the same computing resources.

https://doi.org/10.2172/1845709

Continuum-computing capabilities can enable scientific discovery from a broad range of data sources—i.e. HPC simulations, experiments, scientific instruments, and sensor networks—over a wide scale of computing platforms: leadership-class HPC, clusters, clouds, workstations, and devices at the edge. Continuum-computing capabilities can also manage large data volumes from computations and experiments to minimize data movement, save storage space, and boost resource efficiency—often while simultaneously increasing scientific precision.

This program solicits research to advance continuum-computing capabilities to run on a variety of computing platforms and at different length and time scales; to be automated and controllable; to be more interoperable and composable; and to use provenance and metadata for transparent results. This program also solicits co-designed research activities for continuum computing as well as new management and coordination algorithms.

• Storage Systems and I/O:

The success of the DOE computational, experimental, and observational sciences is inextricably tied to the usability, performance, and reliability of emerging storage systems and input/output (SSIO) technologies. Emerging technologies include storage and networking devices, including those providing computational capabilities. SSIO technologies involve the organization, movement, placement, and efficient retrieval of data to enhance computation and discovery. This includes innovative interfaces and management methods that allow for flexible, high-performance access to large data sets, potentially federated across different kinds of memory, edge devices, and repositories, capturing and management relevant usage statistics, provenance, and other metadata.

This program solicits research to improve SSIO capabilities that enable science understandability and reproducibility; accelerate scientific discovery; enhance SSIO usability, performance, and resilience; and improve efficiency and integrity of data movement and storage. One particular focus of this program is to improve pipelines for analysis-centric, data intensive workflows on HPC systems, and that use large-scale storage. This program also solicits techniques and tools for advancing findable, accessible, interoperable reusable (FAIR) data practices of management, archiving, curation, and/or reuse, of data generated by experimental, observational, and simulation relevant to SC mission areas. Additional areas of interest include combining of data streaming and cloud storage uses for SC infrastructure as well as visualization needs at the edge for SC experimental facilities. Possible topics are highlighted in the “Report for the ASCR Workshop on the Management and Storage of Scientific Data”, https://doi.org/10.2172/1845707.

• Programming Models, Environments, and Portability:

Innovative programming models for developing applications on next-generation platforms, exploiting unprecedented parallelism, heterogeneity of memory systems (e.g.

non-uniform memory access [NUMA], non-coherent shared memory, high-bandwidth https://doi.org/10.2172/1845707 memory [HBM]), scratchpads, and heterogeneity of processing (e.g., graphics processing units [GPUs], field- programmable gate arrays [FPGAs], coarse-grained reconfigurable architectures [CGRAs], other types of accelerators, big-small cores, processing in memory, and near memory, etc.), with particular emphasis on making it easier to program at scale. Basic research on programming tools, for all phases of the software-development cycle, are relevant, including but not limited to, design, implementation, verification, optimization, and integration. Particularly welcome are methods that infuse artificial intelligence/machine learning into the programming environment.

Work on programming models, environments, and portability is often informed by considerations stemming from the collaborative nature of the modern scientific enterprise. See the report, “Basic Research Needs in The Science of Scientific Software Development and Use: Investment in Software is Investment in Science”, https://doi.org/10.2172/1846009.

• Operating and Runtime Systems:

System software that provides intelligent, adaptive resource management and support for highly-parallel software and workflow-management systems, and that facilitates effective and efficient use of heterogeneous computing technologies, including diverse execution models, processors, accelerators, memory, and storage systems. Target workloads include modeling and simulation, data analysis, and the processing of large-scale, streaming data from experiments.

• Performance Portability and Co-design:

Methods that support performance portability, which provides the ability to efficiently use diverse kinds of hardware platforms with minimal changes to the application source code, and/or hardware/software co-design, which is a method for designing and/or adapting both hardware and software design as part of a holistic process. These methods include automated and semi-automated refinements from high-level specification of an application and/or hardware design to low-level code, optimized when compiled and/or, for software, at runtime, to different HPC platforms. The focus is on enabling performance portability of, and/or the design of future hardware for, applications developed for extreme-scale computing and beyond. Possible topics are highlighted in, “Reimagining Codesign for Advanced Scientific Computing: Report for the ASCR Workshop on Reimagining Codesign”, https://doi.org/10.2172/1822199.

• Distributed Scheduling and Resource Management:

As scientific-computing resources are being called upon to support a wide variety of workloads, including those that tightly integrate large-scale and ensemble simulation and data-analysis workflows with experimental data collection and control, the algorithms and implementations matching computational requirements to resources need to scale to handle more tasks, more resources, and more-widely-distributed resources. Specifically sought are methods for decentralized, resilient, secure resource https://doi.org/10.2172/1846009 https://doi.org/10.2172/1822199 management, scheduling, and coupled data transfer across widely distributed computing facilities; and modeling of such distributed systems.

• Network-Offloaded Acceleration for Distributed/Parallel Computing:

Programmable and computation-enabled network interfaces present the opportunity to exploit computational power closer to the network to complement the capabilities of CPUs, GPUs, and other computational components. Note that the programmable network interfaces include both edge accelerators as well as devices in core interconnects in parallel platforms or transport planes in distributed settings.

Application behavioral information may be exploited, both in terms of dynamic learning as well as mathematically predefined primitives such as distributed reductions and other offloaded synchronization operations. New methods, algorithms, software, and interfaces are needed to effectively exploit asynchronous and autonomous capabilities of network hardware beyond traditional data-transfer functionalities. Of interest are new conceptual approaches, algorithmic support, application programming interfaces, and use cases in HPC scientific applications.

• Computer Science Fundamentals Accounting for Thermodynamics and Energy:

Unprecedented levels of modern computation, including areas such as artificial intelligence and machine learning (AI/ML) training, have now made computation a very large consumer of energy in the Nation and the world. Much of modern computer science, and the understanding it provides regarding the fundamental properties of algorithms, does not account for the underlying thermodynamic and information-theoretic reality of computation. As “Beyond Moore” devices are explored along with their corresponding ultra-efficient computer architectures, and the programming paradigms appropriate for these new computing technologies, a better understanding is needed of both potential ultra-efficient computer architectures and the energy-aware properties of algorithms executed on them. Ultra-efficient computer architectures include, but are not limited to, those based on reversible and asymptotically-adiabatic approaches. Investigations combining thermodynamics and information theory, computer architecture, reversible computing and algorithmic properties are sought to advance our ability to design new, energy-efficient approaches to scientific computation.

• Memory-Aware Systems:

Advances in memory technologies are creating new opportunities and challenges where it is unclear how to best introduce or abstract memory awareness and composition.

Memory is evolving in highly asymmetric and distributed directions, with new industry standards greatly expanding memory sharing and capacities to much larger sizes, largely in backward-compatible system architectures. Research is needed to uncover new possibilities for solving larger scientific-computing problems with such highly asymmetric and distributed memory architectures. Innovations in algorithms, software interfaces, programming languages and models are needed to also effectively exploit new processing-in-memory architectures that are emerging as a paradigm for scientific computing. Memory safety needs to be revisited in fundamental research on programming languages, runtimes, and operating systems, considering the multi-developer and shared nature of modern scientific programming eco-systems. The smoothening of the spectrum from volatile to non-volatile memories needs to be investigated for revisiting out-of-core algorithms to expand the limits of scientific computing. On-the-fly compression and decompression needs investigation for increasing the problem sizes without detriment to performance. The intersection of machine learning (ML) with memory systems opens the potential for new solutions, including smarter ML-informed cache prefetching and replacement policies potentially customizable for specific scientific applications via signatures and other mechanisms.

• Quantum Computing:

Research to develop modules of end-to-end software toolchains aimed to program and control quantum computing systems at scale. Possible topics include quantum computing algorithms and the areas as presented in “Report for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking,” https://doi.org/10.2172/2001045.

Notice of Submission Requirements for the Panel Review on the topic “Quantum error detection, prevention, protection, and correction protocols across the quantum software stack” with specific emphasis of codesign of quantum algorithms for applications within the DOE SC mission space. ASCR expects to convene a merit-review panel in March 2026 for applications submitted in this area. To be considered by the panel, a pre-application must be submitted by November 14, 2025. A pre-application may involve researchers from a single or multiple eligible institutions. Each pre-application will be reviewed for responsiveness and competitiveness of the proposed research. ASCR expects to provide pre-application encouragement and discouragement decisions by December 18, 2025. To be reviewed by the panel, an application must be associated with an encouraged pre-application, submitted by January 28, 2026, and request no more than $600,000 in total across all institutions for a two-year award. ASCR expects to make at most three awards.

Contacts:

• Marco Fornari, marco.fornari@science.doe.gov

• Pavel Lougovski, pavel.lougovski@science.doe.gov

• Quantum Networking:

This topic involves innovative research in quantum networking concepts, systems, and protocols by which quantum networking applies in scientific discovery, including, but not limited to, distribution of quantum information from sensors, quantum networking in support of interconnected or scalable quantum computing systems, and blind/cloud quantum computing. Networking can span heterogeneous systems or homogeneous systems (such as all-photonic) and parallel quantum processing (in co-located or local-https://doi.org/10.2172/2001045 mailto:pavel.lougovski@science.doe.gov area settings) and distributed quantum communications (at metropolitan or wide-area scales). Possible topics include quantum networking areas as presented in “Report for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking,” https://doi.org/10.2172/2001045.

This program also supports:

• Participation in International Standardization:

Scientific computing relies on robust adoption of Voluntary Consensus Standards2

(VCSs) that are applicable to state-of-the-art computing technologies. Notably, most applications running at the ASCR user facilities depend on some combination of standardized programming languages and application programming interfaces (APIs), and DOE contributes to many of them, including, but not limited to, the Message Passing Interface (MPI), C, C++, Fortran, OpenMP, and SYCL. Moreover, standardization is an important enabler of knowledge transfer from research to industry.

Similarly, the characterization of computing hardware relies on benchmarks established through a VCS process, and these benchmarks drive industry decisions affecting what capabilities ASCR user facilities can provide. Such benchmarks include, but are not limited to, SPEC CPU/ACCEL and MLPerf. VCSs and benchmarks relevant to data, artificial intelligence and machine learning, quantum computing, software, and hardware interfaces are all in scope.

The development of standards relies on robust participation from a broad spectrum of Stakeholders, and the program supports maintaining and broadening participation in standards development. Standards development benefits from the participation of laboratory and university researchers in addition to experts from businesses of all sizes.

Funding may support training on standards development and leadership, travel to relevant meetings, the hosting of relevant meetings, the development of applications for, and associated prototypes of, new standardized functionalities, and any Standards Development Activity3. Particularly welcome are activities supporting US leadership in standards development and activities including a specific focus on broadening participation from experts from traditionally underrepresented groups, academic institutions, small businesses, and others who may face higher participation barriers.

Topics that are out of scope for Computer Science include:

2 Voluntary Consensus Standards are “Standards [that] are developed through a process that is open to participation by representatives of all interested parties, transparent, consensus-based, and subject to due process. These might be developed by governmental organization or private sector groups such as the American Society for Testing and Materials (ASTM) or the International Organization for Standardization (ISO).” See https://www.directives.doe.gov/terms_definitions/voluntary-consensus-standard; for additional discussion, see Office of Management and Budget Circular Number A-119, https://www.nist.gov/system/files/revised_circular_a-119_as_of_01-22-2016.pdf.

3 Standards Development Activity is defined in 15 USC § 4301(a)(7). See https://www.govinfo.gov/app/details/USCODE-2015-title15/USCODE-2015-title15-chap69-sec4301/summary https://doi.org/10.2172/2001045 https://www.directives.doe.gov/terms_definitions/voluntary-consensus-standard https://www.nist.gov/system/files/revised_circular_a-119_as_of_01-22-2016.pdf https://www.govinfo.gov/app/details/USCODE-2015-title15/USCODE-2015-title15-chap69-sec4301/summary

• Topics not covered in the list of Computer Science Priority Interests, above, except with the specific encouragement of a Computer Science program manager in response to an emailed concept paper;

• Research with primary emphasis on resilient solvers, and/or new development of machine probabilistic methods and their mathematical formalism;

• Research aimed at advancing computer-supported collaboration, social computing, and generalized research in human-computer interaction;

• Discipline-specific data analytics and informatics without a clear articulation of how the research will generalize to other disciplines and/or advance computer-science capabilities;

• Research focused on the World Wide Web, the dark web, and/or data about it;

• Research that is primarily to advance cloud computing, hand-held, portable, desktop, and/or embedded computing that is not applicable to ASCR-supported computational and data science environments;

• Research and applications not motivated and justified in the context of current and future SC user facilities, especially those supported by ASCR (i.e., Argonne Leadership Computing Facility or ALCF, Oak Ridge Leadership Computing Facility or OLCF, and National Energy Research Scientific Computing Center or NERSC):

https://science.osti.gov/ascr/Facilities;

• Development of new candidate physical qubit systems and improvements to physical qubits; and

• Quantum key distribution, quantum cryptography and cryptanalysis.

Submission of preliminary research descriptions (e.g., pre-applications, concept papers) is strongly encouraged. They will be reviewed for responsiveness of the proposed work to the research topics. You must send an email to a subprogram contact for information regarding format and content.

Subprogram Contacts:

• David Rabson, david.rabson@science.doe.gov and Hal Finkel, hal.finkel@science.doe.gov: artificial intelligence and machine learning for science, data analysis and visualization

• Hal Finkel, hal.finkel@science.doe.gov: Storage Systems and I/O (SSIO); programming models, environments, and portability; operating and runtime systems; performance portability and co-design; distributed scheduling and resource management

• Hal Finkel, hal.finkel@science.doe.gov: Continuum computing, participation in international standardization and activities supporting career development, and broadening participation, in computer-science research

• David Rabson, david.rabson@science.doe.gov and Hal Finkel, hal.finkel@science.doe.gov: Network-Offloaded Acceleration for Distributed/Parallel Computing, Computer Science Fundamentals Accounting for Thermodynamics and Energy, Memory-Aware Systems

• Marco Fornari, marco.fornari@science.doe.gov and Pavel Lougovski, https://science.osti.gov/ascr/Facilities mailto:david.rabson@science.doe.gov mailto:hal.finkel@science.doe.gov mailto:hal.finkel@science.doe.gov mailto:hal.finkel@science.doe.gov mailto:david.rabson@science.doe.gov mailto:hal.finkel@science.doe.gov mailto:Marco.Fornari@science.doe.gov pavel.lougovski@science.doe.gov: Quantum Computing and Quantum Networking Website: https://science.osti.gov/ascr/Research/Computer-Science;

https://science.osti.gov/ascr/Community-Resources/Program-Documents

(c) Computational Partnerships

This activity primarily supports the Scientific Discovery through Advanced Computing (SciDAC), program, which is a recognized leader for the employment of HPC for scientific discovery. Established in 2001, SciDAC involves ASCR partnerships with the other SC programs, other DOE program offices, and other federal agencies in strategic areas with a goal to dramatically accelerate progress in scientific computing through strong collaborations between discipline scientists, applied mathematicians, and computer scientists. For examples of current SciDAC partnerships, refer to the website https://www.scidac.gov.

Applications to SciDAC that involve software products should demonstrate the need for the software being developed in one or more scientific communities and should address both the dissemination of the software and the strategy for the software’s long-term sustainability after the end of the proposed activities.

Other partnerships between discipline scientists, applied mathematicians, and computer scientists are also supported.

Subprogram Contacts:

• Robinson Pino, robinson.pino@science.doe.gov and Ravinder Kapoor, ravinder.kapoor@science.doe.gov, SciDAC Institutes

• Marco Fornari, marco.fornari@science.doe.gov, David Rabson, david.rabson@science.doe.gov, and Robinson Pino, robinson.pino@science.doe.gov, SciDAC and other partnerships

Website: https://science.osti.gov/ascr/Research/scidac

(d) Advanced Computing Technologies

This activity supports the Research and Evaluation of Prototypes (REP), including in quantum computing and networking. The REP activity addresses the challenges of next generation computing systems. By actively partnering with the research community, including industry and Federal agencies, on the development of technologies that enable next-generation machines, ASCR ensures that commercially available architectures serve the needs of the scientific community. The REP activity also prepares researchers to effectively use future generation of scientific computers, including novel technologies, and seeks to reduce risk for future major procurements.

Additionally, this subprogram provides graduate research training for the next generation of mailto:Pavel.Lougovski@science.doe.gov https://science.osti.gov/ascr/Research/Computer-Science https://science.osti.gov/ascr/Community-Resources/Program-Documents https://www.scidac.gov/ mailto:robinson.pino@science.doe.gov mailto:ravinder.kapoor@science.doe.gov mailto:marco.fornari@science.doe.gov mailto:david.rabson@science.doe.gov mailto:Robinson.Pino@science.doe.gov https://science.osti.gov/ascr/Research/scidac scientists as well as activities supporting career development, and broadening participation, in high-end computational science.

Research topics currently of interest for Advanced Computing Technologies (ACT) include:

• Research focused on information processing and computation systems for emerging computing technologies (including quantum computing and networking technologies) which aim to enable testbed use, including hardware architectures, accelerators, development of programming environments, languages, libraries, compilers, simulators and other modeling tools, and research and development on their algorithms for physical simulation and capability assessment.

• Neuromorphic computing: Specific to HPC-enabled modeling and simulation of computing architecture at extreme scales for generalizable applications of the proposed approach and for the prototyping and fabrication of advanced neuromorphic computing architectures.

• Microelectronics for scientific computing, including innovative methods for processor synthesis, placement, architectures, and algorithms. Especially of interest are multi-disciplinary co-design projects where each scientific discipline informs and engages the other to achieve orders of magnitude improvements in system-level performance.

• Advanced Wireless Networks: Next generation wireless networks could enable scientific facilities to become more mobile, remotely manageable, and distributed. Breakthroughs in new software frameworks, tools, and approaches are needed to broaden and extend those wireless networks into existing or new scientific domains. By leveraging next-generation advanced wireless technology and microelectronics, we can build the tools, applications, and infrastructure needed to explore, understand, and harness new scientific discoveries.

• The maintenance and improvement of the software ecosystem, including that developed through the Exascale Computing Project (ECP), which provides shared software packages, novel evaluation systems, and applications relevant to the science and engineering requirements of DOE, in order that the full potential of the current and future computing systems deployed by DOE can be continuously realized.

• Robotics and Scientific Discovery Automation: Development and prototyping of autonomous and semi-autonomous robotic systems tightly integrated with AI/ML, high-performance computing, and real-time sensor data to enable closed-loop experimentation and laboratory automation at scale.