Project Grant 2119640
- This $400,000 federal Project Grant award from the National Science Foundation's (NSF) Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) program (CFDA 47.041, Engineering) aims to revolutionize the design and manufacturing of advanced nanocomposite materials using artificial intelligence (AI). The research focus is on understanding and controlling amorphous-crystalline interfaces within these materials, which can enhance their strength, durability, and reliability. The award...
- The National Science Foundation (NSF) Division of Materials Research awarded a $262,500 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the Georgia Tech Research Corporation (Georgia Tech) for the "DMREF/Collaborative Research: Active Learning-based Material Discovery for 3D Printed Solids with Locally-Tunable Electrical and Mechanical Properties" project. This multi-disciplinary effort aims to establish an active learning approach to rapidly...
- This Project Grant award for $313,087 from the National Science Foundation's Engineering program (CFDA 47.041) will support a collaborative research initiative at the State University of New York at Binghamton to develop a physics-informed machine learning framework for tailoring the multidirectional mechanical properties of composite materials. The research aims to create a data-driven approach to understand the relationship between material architecture and mechanical behavior, facilitating...
- This National Science Foundation (NSF) Designing Materials to Revolutionize and Engineer our Future (DMREF) Project Grant award, valued at $150,000 and running from October 1, 2023 to September 30, 2027, supports collaborative research to develop simulation-informed models for additive manufacturing of amorphous metals. The research team at The Washington University aims to derive meaningful measures of material structure from electron nanodiffraction and simulation data, and build predictive...
- This National Science Foundation (NSF) Project Grant award through the Engineering program (CFDA 47.041) will support research to develop a physics-constrained artificial intelligence (PCAI) framework for real-time control and optimization of additive manufactured (AM) metal components. The $262,929 award to the University of Maryland, College Park will establish an in-situ processing data-driven approach to effectively link manufacturing processes to environmentally-related performance for...
- This Project Grant award of $500,000 from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program supports the development of a computational pipeline that integrates AI-driven optimization techniques with advanced simulations to streamline the design of mechanical metamaterials (MMs). The goal is to automate and accelerate the complex, iterative design process for these materials, which exhibit unique mechanical properties and have...
- This $375,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to accelerate the discovery, design, and implementation of new engineered photonic materials, particularly photonic metamaterials, through a data-driven deep learning approach. The project, led by the Georgia Tech Research Corporation, will establish deep learning frameworks to construct photonic metamaterials, integrate information on tailorable optical...
- This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) provides $425,943 to Arizona State University to develop an AI-enabled automated workflow for designing ultrastrong and ultraelastic metallic alloys. The research team aims to leverage artificial intelligence, computational modeling, and experimental tools to rapidly design, synthesize, and test these complex concentrated alloys. The innovative strategies developed through...
- The National Science Foundation Division of Materials Research awarded a $1.8 million Project Grant to the Texas A&M Engineering Experiment Station (doing business as Tees) to support the "DMREF: AI-GUIDED ACCELERATED DISCOVERY OF MULTI-PRINCIPAL ELEMENT MULTI-FUNCTIONAL ALLOYS" project. The funding period is from October 1, 2021 to September 30, 2025. Under this award, Tees will utilize artificial intelligence-guided methods to rapidly discover new multi-principal element alloys...
- The National Science Foundation (NSF) awarded a $399,916 Project Grant under its Engineering program (CFDA 47.041) to the University of Nevada, Reno (UNR) to fund research on developing ultrastrong and ultraelastic metallic alloys using artificial intelligence (AI) enabled automated design. The goal of this 16-month project is to leverage AI, computational modeling, and experimental tools to rapidly design, synthesize, and test new metallic alloy compositions that can withstand extreme stress...
This Project Grant award from the National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation provides $1,177,594 to support research titled "DMREF/GOALI/COLLABORATIVE RESEARCH: PHYSICS-INFORMED ARTIFICIAL INTELLIGENCE FOR PARALLEL DESIGN OF METAL MATRIX COMPOSITES AND THEIR ADDITIVE MANUFACTURING." The funding period is from September 1, 2021 to August 31, 2025. The research is being conducted under the NSF Engineering program (CFDA 47.041), which aims to foster innovation in engineering research and education. Specifically, the grant funds collaborative research between Georgia Tech and its sub-awardee, the Georgia Tech Research Corporation. This research will develop physics-informed artificial intelligence techniques to parallelize the design and additive manufacturing processes for metal matrix composites. The goal is to accelerate development and deployment of these advanced materials.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $1.2m | 8/24/21 |