Closed · DE-FOA-0001956 · CFDA 81.087 · Discretionary
Machine Learning for Geothermal Energy
Federal grant opportunity posted by Golden Field Office, cataloged on Grants.gov.
- $500K-$700K
- Award range
- Closed
- Status
- November 13, 2018
- Close date
- 6
- Expected awards
The verdict
Machine Learning for Geothermal Energy is a closed discretionary listing from Golden Field Office that offered $500,000 -- $700,000 across 6 expected awards. Future funding cycles may be published under the same CFDA number. PlainGrants indexes 839 other opportunities from Golden Field Office.
- $500K-$700K
- award range
- Closed
- application status
- 6
- expected awards
- 81.087
- CFDA program
Archived opportunity record
Machine Learning for Geothermal Energy is closed (last close November 13, 2018). Check the agency catalog for replacement NOFOs. Golden Field Office still indexes 839 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 November 13, 2018
Est. Total Funding
$3,600,000
Expected Awards
6
Instrument
Cooperative Agreement
Cost Sharing
Required
Description
The purpose of this modification is to extend the application due date. Please see the table of changes on the second page of Modification 0001 on the EERE Exchange website at https://eere-exchange.energy.gov. Complete information on this FOA can be found on the EERE Exchange website - https://eere-exchange.energy.gov. The U.S. Department of Energy’s Geothermal Technology Office (GTO) Machine Learning for Geothermal Energy funding opportunity announcement (FOA) supports projects that will develop new analytical tools for finding and developing geothermal resources and establish the practice of machine learning in geothermal operations. The rapidly advancing field of Machine Learning (ML) offers substantial opportunities for technology advancement and cost reduction throughout the geothermal project lifecycle, from resource exploration to power plant operations. Under this funding opportunity, GTO is interested in two topic areas: Topic 1: Machine Learning for Geothermal Exploration - GTO seeks projects that advance geothermal exploration through the application of machine learning techniques to geological, geophysical, geochemical, borehole, and other relevant datasets. Of particular interest to GTO are projects that will identify data acquisition targets and build community datasets for future work. Topic 2: Advanced Analytics for Efficiency and Automation in Geothermal Operations - GTO seeks projects that apply advanced analytics to power plant and other operator datasets, with the goal of improving operations and resource management. Complete information on this FOA can be found on the EERE Exchange website - https://eere-exchange.energy.gov. For questions and answers pertaining to this FOA, please reference the DE-FOA-0001956 Machine Learning FAQ Log in FOA Documents. The eXCHANGE system is currently designed to enforce hard deadlines for Concept Paper and Full Application submissions. The APPLY and SUBMIT buttons automatically disable at the defined submission deadlines. The intention of this design is to consistently enforce a standard deadline for all applicants. Applicants that experience issues with submissions PRIOR to the FOA Deadline: In the event that an Applicant experiences technical difficulties with a submission, the Applicant should contact the eXCHANGE helpdesk for assistance (exchangehelp@hq.doe.gov). The eXCHANGE helpdesk and/or the EERE eXCHANGE System Administrators (eXCHANGE@ee.doe.gov) will assist the Applicant in resolving all issues. Applicants that experience issues with submissions that result in a late submission: In the event that an Applicant experiences technical difficulties with a submission that results in a late submission, the Applicant should contact the eXCHANGE helpdesk for assistance (exchangehelp@hq.doe.gov). The eXCHANGE helpdesk and/or the EERE eXCHANGE System Administrators (eXCHANGE@ee.doe.gov) will assist the Applicant in resolving all issues (including finalizing the submission on behalf of, and with the Applicant's concurrence). DOE will only accept late applications when the Applicant has a) encountered technical difficulties beyond their control; b) has contacted the eXCHANGE helpdesk for assistance; and c) has submitted the application through eXCHANGE within 24 hours of the FOA's posted deadline.
Catch-all / unrestricted eligibility mark · DE-FOA-0001956
Grants.gov marks Machine Learning for Geothermal Energy 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. Applicant cost-sharing is required on the Grants.gov extract. Instrument: Cooperative Agreement. Funding-activity code maps to Energy (EN). This eligibility-code string dominates the Golden Field Office book (634 peers, 75% of 840).
- 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.
Agency Contact
Michael J. Weathers machinelearninggeo@ee.doe.gov
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What to do next
Machine Learning for Geothermal Energy is closed; use the parent program and agency catalog for the next funding cycle.
- Browse open federal opportunities in the same category when this listing is closed. Open opportunities
- Look up CFDA 81.087 across all indexed listings before you commit to this opportunity alone. Eligibility finder
- Compare award ranges across federal categories before you pick a single CFDA. All categories
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 |