Closed · DE-FOA-0002958 · CFDA 81.049 · Discretionary
Scientific Machine Learning for Complex Systems
Federal grant opportunity posted by Office of Science, cataloged on Grants.gov.
- $300K-$1.2M
- Award range
- Closed
- Status
- April 19, 2023
- Close date
- -
- Expected awards
The verdict
Scientific Machine Learning for Complex Systems is a closed discretionary listing from Office of Science that offered $300,000 -- $1,200,000. Future funding cycles may be published under the same CFDA number. PlainGrants indexes 414 other opportunities from Office of Science.
- $300K-$1.2M
- award range
- Closed
- application status
- 81.049
- CFDA program
- 414
- other opportunities from Office of Science
Archived opportunity record
Scientific Machine Learning for Complex Syste… is closed (last close April 19, 2023). The parent program page for CFDA 81.049 is the reliable place to catch re-announcements. Office of Science still indexes 414 other opportunities here.
According to Grants.gov (extract vintage 2026-08-10), this record reflects the officially published listing. See methodology.
Deadline tracker
Application window closed
Closed April 19, 2023
Est. Total Funding
$16,000,000
Instrument
Grant
Description
The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in research applications to explore potentially high-impact approaches in the development and use of scientific machine learning (SciML) and artificial intelligence (AI) in the predictive modeling, simulation and analysis of complex systems and processes.High-performance computational models, simulations, algorithms, data from experiments and observations, and automation are being used to accelerate scientific discovery and innovation. Recent workshops, report, and strategic plans across the DOE have highlighted the research, development, and use of artificial intelligence and machine learning for science, energy, and security. Relevant domains include materials, environmental, and life sciences; high-energy, nuclear, and plasma physics; and the DOE Energy Earthshots Initiative, for examples. A 2018 Basic Research Needs workshop and report on scientific machine learning (SciML) and AI identified six Priority Research Directions (PRDs) for the development of the broad foundations and research capabilities needed to address such DOE mission priorities. The first three PRDs for foundational research are a set of themes common to all SciML approaches and correspond to the need for domain-awareness, interpretability, and robustness and scalability, respectively. Of the other three PRDs for capability research, PRD #5 (Machine Learning-Enhanced Modeling and Simulation) and uncertainty quantification are the subject of this FOA.DOE is committed to promoting the diversity of investigators and institutions it supports, as indicated by the ongoing use of program policy factors (see Section V) in making selections of awards. To strengthen this commitment, DOE encourages applications that are led by, or include partners from Established Program to Stimulate Competitive Research (EPSCoR) states, that are underrepresented in the ASCR portfolio and applications led by individuals from groups historically underrepresented in STEM.
Catch-all / unrestricted eligibility mark · DE-FOA-0002958
Grants.gov marks Scientific Machine Learning for Complex Syste… with a catch-all / unrestricted eligibility code (Unrestricted / see official listing). Read the official announcement text for the real applicant filter — the code alone is not a door list. The extract does not mark applicant cost-sharing as required. Instrument: Grant. Funding-activity code maps to Science and Technology / R&D (ST). This eligibility-code string dominates the Office of Science book (278 peers, 67% of 415).
- Code 99
- Unrestricted / see official listing
Codes are Grants.gov eligibility category values from the extract vintage 2026-08-10. Confirm the full eligibility statement on the official listing or the Grants.gov eligibility reference.
Official Listing on Grants.gov
View full details, application forms, and submission instructions.
Parent Grant Program
Basic Energy Sciences
U.S. Department of Energy
Agency Contact
Steven.Lee@science.doe.gov
More from Office of Science
What to do next
Scientific Machine Learning for Complex Syste… is closed; use the parent program and agency catalog for the next funding cycle.
- Parent CFDA 81.049 may publish future NOFOs under the same assistance listing. Open program
- Basic Energy Sciences indexes 6 Grants.gov listings in this extract. Program profile
- Office of Science lists 414 other indexed opportunities on PlainGrants. Agency catalog
Opportunity deadlines and eligibility change on Grants.gov; PlainGrants mirrors the extract and is not the submission system.
Disclaimer: This information is sourced from Grants.gov and SAM.gov and is for informational purposes only. Opportunity details, deadlines, and eligibility requirements change frequently. Always verify current information directly on Grants.gov before applying. PlainGrants is not affiliated with any federal agency.
Read our methodology - how this data is sourced, computed, and verified.
| Publisher | PlainGrants |
| Sources | the SAM.gov Assistance Listings and Grants.gov |