2423569
Project Grant
Overview
Grant Description
Sbir phase I: materials science digital experts and AI-powered data platform -the broader/commercial impact of this SBIR phase I project lies in its potential to significantly streamline the process of discovering and utilizing novel materials, vital for advancements in sectors like healthcare, energy, and national defense.
A large portion of essential materials data is currently inaccessible, hidden within complex documents or known only to a handful of experts. This project aims to develop a technology that transforms this inaccessible data into useful information, drastically reducing the time needed for material selection from weeks to minutes, thereby accelerating scientific and technological advancement and enhancing national prosperity and security.
The market for advanced materials is projected to grow to $2.1 trillion by 2025, and the business model for this initiative focuses on providing technological services to materials suppliers, ensuring a sustainable competitive advantage by improving access to and usability of critical data. Initially targeting the semiconductor industry and industries reliant on polymers, the strategy is to achieve significant market penetration, with anticipated substantial annual revenues by the third year of production, underlining its impact across multiple high-value industries.
This small business innovation research (SBIR) phase I project addresses the critical challenge of dark data in materials science?valuable data that is unutilized because it is trapped in diverse formats or accessible only to a few experts. The primary research objective is to develop an artificial intelligence-driven platform capable of extracting and synthesizing this data into an accessible and interpretable format.
The proposed research involves the creation of a customizable, conversational digital expert system that leverages advanced large language models (LLMs) to interact with and learn from heterogeneous data sources, including natural language texts and inconsistent file formats. This system will enable the transformation of complex datasets into structured, actionable insights, facilitating rapid and accurate materials selection and application.
The anticipated technical results include the successful demonstration of the platform's ability to interpret and organize large volumes of dark data, significantly reducing the time and expertise required to access this information. This breakthrough has the potential to catalyze discoveries and innovations in materials science by making decades of accumulated data readily available for research and commercial use. 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.
A large portion of essential materials data is currently inaccessible, hidden within complex documents or known only to a handful of experts. This project aims to develop a technology that transforms this inaccessible data into useful information, drastically reducing the time needed for material selection from weeks to minutes, thereby accelerating scientific and technological advancement and enhancing national prosperity and security.
The market for advanced materials is projected to grow to $2.1 trillion by 2025, and the business model for this initiative focuses on providing technological services to materials suppliers, ensuring a sustainable competitive advantage by improving access to and usability of critical data. Initially targeting the semiconductor industry and industries reliant on polymers, the strategy is to achieve significant market penetration, with anticipated substantial annual revenues by the third year of production, underlining its impact across multiple high-value industries.
This small business innovation research (SBIR) phase I project addresses the critical challenge of dark data in materials science?valuable data that is unutilized because it is trapped in diverse formats or accessible only to a few experts. The primary research objective is to develop an artificial intelligence-driven platform capable of extracting and synthesizing this data into an accessible and interpretable format.
The proposed research involves the creation of a customizable, conversational digital expert system that leverages advanced large language models (LLMs) to interact with and learn from heterogeneous data sources, including natural language texts and inconsistent file formats. This system will enable the transformation of complex datasets into structured, actionable insights, facilitating rapid and accurate materials selection and application.
The anticipated technical results include the successful demonstration of the platform's ability to interpret and organize large volumes of dark data, significantly reducing the time and expertise required to access this information. This breakthrough has the potential to catalyze discoveries and innovations in materials science by making decades of accumulated data readily available for research and commercial use. 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 Agency
Place of Performance
Cambridge,
Massachusetts
02139-2723
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the End Date has been extended from 11/30/24 to 09/30/25.
FUM Technologies was awarded
Project Grant 2423569
worth $275,000
from in June 2024 with work to be completed primarily in Cambridge Massachusetts United States.
The grant
has a duration of 1 year 3 months 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: Materials Science Digital Experts and AI-Powered Data Platform
Abstract
The broader/commercial impact of this SBIR Phase I project lies in its potential to significantly streamline the process of discovering and utilizing novel materials, vital for advancements in sectors like healthcare, energy, and national defense. A large portion of essential materials data is currently inaccessible, hidden within complex documents or known only to a handful of experts. This project aims to develop a technology that transforms this inaccessible data into useful information, drastically reducing the time needed for material selection from weeks to minutes, thereby accelerating scientific and technological advancement and enhancing national prosperity and security. The market for advanced materials is projected to grow to $2.1 trillion by 2025, and the business model for this initiative focuses on providing technological services to materials suppliers, ensuring a sustainable competitive advantage by improving access to and usability of critical data. Initially targeting the semiconductor industry and industries reliant on polymers, the strategy is to achieve significant market penetration, with anticipated substantial annual revenues by the third year of production, underlining its impact across multiple high-value industries.
This Small Business Innovation Research (SBIR) Phase I project addresses the critical challenge of dark data in materials science—valuable data that is unutilized because it is trapped in diverse formats or accessible only to a few experts. The primary research objective is to develop an artificial intelligence-driven platform capable of extracting and synthesizing this data into an accessible and interpretable format. The proposed research involves the creation of a customizable, conversational digital expert system that leverages advanced Large Language Models (LLMs) to interact with and learn from heterogeneous data sources, including natural language texts and inconsistent file formats. This system will enable the transformation of complex datasets into structured, actionable insights, facilitating rapid and accurate materials selection and application. The anticipated technical results include the successful demonstration of the platform's ability to interpret and organize large volumes of dark data, significantly reducing the time and expertise required to access this information. This breakthrough has the potential to catalyze discoveries and innovations in materials science by making decades of accumulated data readily available for research and commercial use.
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
HC
Solicitation Number
NSF 23-515
Status
(Ongoing)
Last Modified 4/4/25
Period of Performance
6/15/24
Start Date
9/30/25
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2423569
Additional Detail
Award ID FAIN
2423569
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
W5HBG2REYJP3
Awardee CAGE
9W0X7
Performance District
MA-07
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Modified: 4/4/25