2415734
Project Grant
Overview
Grant Description
SBIR Phase I: AI systems and methods for critical natural resource development.
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to accelerate the development of mineral and energy resources critical to the US economy and electrification of global energy.
Improved mineral targeting and screening will increase the effectiveness of each dollar spent on exploration for copper, nickel, cobalt, and critical rare-earth minerals.
More effective drill-targeting can shorten the time required to measure a deposit by several years, helping to get critical supply into the market sooner.
Applying AI to the design of carbon storage and geothermal reservoirs will help generate more energy and store more CO2 while ensuring critical safety requirements can be met with confidence.
This Small Business Innovation Research (SBIR) Phase I project will advance the capabilities of several key AI methods to address challenges for the geosciences and natural resources.
Generative and autonomous decision-making AI have radically changed several important industries from vehicles to biotechnology.
They have the potential to do the same for the geosciences and industries like materials and energy by making it easier to interpret large, high dimensional data and design complex systems for underground resources.
These methods, however, cannot be directly applied without modifications to address the size of geological problems and the significant diversity of data and relatively small amount available.
The company’s approach focuses on improving neural network architecture to improve sample efficiency and to utilize foundation model approaches to reduce training data volume requirements.
The company anticipates that this research will result in a class of state-of-the-art AI methods for geological resources and scientific applications.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to accelerate the development of mineral and energy resources critical to the US economy and electrification of global energy.
Improved mineral targeting and screening will increase the effectiveness of each dollar spent on exploration for copper, nickel, cobalt, and critical rare-earth minerals.
More effective drill-targeting can shorten the time required to measure a deposit by several years, helping to get critical supply into the market sooner.
Applying AI to the design of carbon storage and geothermal reservoirs will help generate more energy and store more CO2 while ensuring critical safety requirements can be met with confidence.
This Small Business Innovation Research (SBIR) Phase I project will advance the capabilities of several key AI methods to address challenges for the geosciences and natural resources.
Generative and autonomous decision-making AI have radically changed several important industries from vehicles to biotechnology.
They have the potential to do the same for the geosciences and industries like materials and energy by making it easier to interpret large, high dimensional data and design complex systems for underground resources.
These methods, however, cannot be directly applied without modifications to address the size of geological problems and the significant diversity of data and relatively small amount available.
The company’s approach focuses on improving neural network architecture to improve sample efficiency and to utilize foundation model approaches to reduce training data volume requirements.
The company anticipates that this research will result in a class of state-of-the-art AI methods for geological resources and scientific applications.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Sunnyvale,
California
94085-3869
United States
Geographic Scope
Single Zip Code
Terra Ai was awarded
Project Grant 2415734
worth $274,361
from National Science Foundation in September 2024 with work to be completed primarily in Sunnyvale California United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: AI Systems and Methods for Critical Natural Resource Development
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to accelerate the development of mineral and energy resources critical to the US economy and electrification of global energy. Improved mineral targeting and screening will increase the effectiveness of each dollar spent on exploration for copper, nickel, cobalt, and critical rare-earth minerals. More effective drill-targeting can shorten the time required to measure a deposit by several years, helping to get critical supply into the market sooner. Applying AI to the design of carbon storage and geothermal reservoirs will help generate more energy and store more CO2 while ensuring critical safety requirements can be met with confidence.
This Small Business Innovation Research (SBIR) Phase I project will advance the capabilities of several key AI methods to address challenges for the geosciences and natural resources. Generative and autonomous decision-making AI have radically changed several important industries from vehicles to biotechnology. They have the potential to do the same for the geosciences and industries like materials and energy by making it easier to interpret large, high dimensional data and design complex systems for underground resources. These methods, however, cannot be directly applied without modifications to address the size of geological problems and the significant diversity of data and relatively small amount available. The company’s approach focuses on improving neural network architecture to improve sample efficiency and to utilize foundation model approaches to reduce training data volume requirements. The company anticipates that this research will result in a class of state-of-the-art AI methods for geological resources and scientific applications.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AI
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 9/17/24
Period of Performance
9/1/24
Start Date
8/31/25
End Date
Funding Split
$274.4K
Federal Obligation
$0.0
Non-Federal Obligation
$274.4K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2415734
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
ETDEBX78DE75
Awardee CAGE
None
Performance District
CA-17
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Modified: 9/17/24