Project Grant 2524729
- This $275,008 Project Grant awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to create an AI-powered framework that helps designers automatically evaluate the environmental impact of their designs and optimize them for sustainability. The key innovations include: Developing novel graph attention network (GAT) algorithms to extract lifecycle inventory (LCI) data by analyzing similarities with existing designs, addressing the issue of LCI data scarcity....
- This National Science Foundation (NSF) Project Grant, awarded under the Engineering (CFDA 47.041) program, aims to create an AI-powered framework to help designers evaluate and optimize the environmental impact of their product designs. The $274,959 award to Florida International University will implement several key innovations: (1) Develop novel graph attention network algorithms to extract lifecycle inventory data for sustainability analysis by leveraging similarities with existing product...
- This $500,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to advance national prosperity and workforce development through research that enables more efficient and cost-effective design of mechanical systems. Specifically, the research seeks to develop a theoretical foundation called "mechanics informatics" that can learn material properties from a single optimized test, instead of many, using advanced information...
- This National Science Foundation (NSF) Engineering program (CFDA 47.041) Project Grant award of $294,457 to The Leland Stanford Junior University (Stanford University) explores the feasibility of using generative artificial intelligence (AI) language models to automate the creation of building energy models. The key objectives are to: 1) test the feasibility of applying generative AI to various steps of the building energy modeling process, and 2) quantify the performance and time trade-offs...
- This $200,000 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) will support research to develop a new computational methodology for analyzing the environmental impacts of complex, dynamic renewable energy systems. The project, led by the University of Vermont, will integrate life cycle analysis and system dynamic modeling to enable accurate and efficient evaluation of the environmental impacts of renewable energy hubs over their lifetimes. This will...
- This $274,192 Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) supports the development of an advanced generative AI toolbox for interactive Life Cycle Assessments (LCA). The primary aim is to create a domain-specific large language model that can process complex, multi-modal data to provide real-time insights into Scope 3 emissions, which are often the most significant part of a company's carbon...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program provides $103,500.00 to the University of Notre Dame to develop a novel AI framework that can automatically discover interpretable scientific insights from large-scale environmental data. The research aims to address the challenge of applying environmental science knowledge discovered at one location or time to understand phenomena occurring elsewhere, which is...
- The $148,060 Project Grant from the National Science Foundation's Division of Civil, Mechanical, and Manufacturing Innovation under the Engineering program (CFDA 47.041) will fund research at Carnegie Mellon University investigating machine learning approaches to support engineering designers in digital manufacturing. The university will mine part designs from open online repositories and curated datasets developed through in-class challenges. A machine learning pipeline will extract design...
- The National Science Foundation (NSF) awarded a $599,995 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) federal grant program to Carnegie Mellon University (CMU). The grant supports a 3-year research project focused on developing sustainable and energy-efficient approaches to large-scale machine learning across domains such as natural language processing, computer vision, and scientific AI applications. The project aims to enhance training efficiency,...
- The National Science Foundation (NSF) Division of Civil, Mechanical, and Manufacturing Innovation awarded a $300,000 Project Grant to the Massachusetts Institute of Technology (MIT) under the NSF Engineering program (CFDA 47.041) to advance the field of engineering design. The project aims to develop a framework for accurately evaluating the novelty and quality of diverse design artifacts, such as CAD drawings, text, sketches, and prototypes, using a combination of human experts and machine...
This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $450,000 to the University of Notre Dame to develop a machine learning-enabled screening tool for estimating the environmental impacts of building materials. The research aims to create a low-cost, user-friendly method that enables small and medium-sized manufacturers to evaluate and improve their production practices without relying on expensive consultants or specialized software. The tool will integrate artificial intelligence with life cycle assessment science to automate environmental impact analysis. A pilot study with a wood product manufacturer will demonstrate and refine the screening method. This project advances the science of environmental assessment in the construction industry by addressing limitations in data quality, modeling methods, and practical usability of existing life cycle assessment tools. The broader goal is to make environmental performance assessment more accessible and affordable, supporting improved sustainability practices across the manufacturing sector.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $450.0k | 8/28/25 |