Cooperative Agreement 2534344
- The National Science Foundation Division of Materials Research awarded a $4 million cooperative agreement to the University of Wisconsin - Madison on September 1, 2026, under the Materials AI and Transformation Through Research Infrastructure for Extreme Environments (MATRIX) Materials Innovation Platform, funded through the Mathematical and Physical Sciences program (CFDA 47.049). MATRIX-MIP provides open-access advanced tools and expertise for discovering and developing materials that...
- The National Science Foundation Division of Materials Research awarded Cornell University a $4 million cooperative agreement on October 1, 2025, under the Mathematical and Physical Sciences program (CFDA 47.049) to advance artificial intelligence-driven materials discovery through the AI Materials Institute (AI-MI). The award funds development of the AI Materials Science Ecosystem (AIMS-EC), an open, cloud-based platform coupling a science-ready large-language model with multimodal data...
- 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 awarded The Johns Hopkins University a $5 million cooperative agreement on August 1, 2026, under the NSF Technology, Innovation, and Partnerships program to develop the AI for Materials Design Network (AIMD-Net), a national test bed of programmable cloud laboratories combining automated robotic experimentation with distributed artificial intelligence. AIMD-Net addresses the challenge of scaling autonomy from individual instruments and laboratories to achieve...
- The National Science Foundation awarded a $300,000 Project Grant to the University of Michigan under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to develop an artificial intelligence framework called the AI-enabled Microstructure Model Builder (AMMBER). AMMBER will autonomously determine input parameters for phase-field models based on a variety of data sources to establish constraints on model parameters. It will leverage automated data pipelines...
- The National Science Foundation awarded Rice University a $4.977 million Cooperative Agreement on August 1, 2026, under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) to develop an AI-driven autonomous materials synthesis platform for next-generation semiconductor and advanced electronic and quantum materials. Rice University will establish a Programmable Cloud Laboratory (PCL) node that integrates robotics, simulations, and automated experiments coordinated by AI...
- The National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation awarded the University of Texas at Austin $600,678 on July 1, 2026, to acquire a multimodal, microelectromechanical systems (MEMS)-based environmental nanoindenter for nanoscale mechanical property characterization of materials under application-critical thermal, electrochemical, and environmental conditions. The nanoindenter will support fundamental and applied materials research in clean energy...
- The National Science Foundation Office of Advanced Cyberinfrastructure awarded Texas A&M Engineering Experiment Station $299,782 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop PhaseForge, an open-source cyberinfrastructure platform for machine-learning-assisted thermodynamic modeling and phase-stability analysis. PhaseForge reframes CALPHAD (Calculation of Phase Diagrams) as an automated, uncertainty-aware, and reproducible...
- 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 Civil, Mechanical, and Manufacturing Innovation awarded The University of Texas At El Paso $550,000 on August 15, 2026, for fundamental research establishing accurate thermal characterization methods for metal additive manufacturing. The award funds investigation of dynamic radiative behavior of materials during powder bed fusion processing under far-from-equilibrium conditions, addressing the limitation that conventional thermal monitoring assumes...
The National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation awarded a $2 million cooperative agreement to Texas A&M Engineering Experiment Station on September 1, 2026, to establish and operate the Autonomous Robotic Metallurgist Materials Innovation Platform (ARM-MIP) at Texas A&M University, an open-access national user facility for alloy discovery and development. The award funds a six-year period of performance through August 31, 2032. ARM-MIP designs, fabricates, tests, and analyzes new alloys by integrating robotics, artificial intelligence, physics-based simulation, and automated experimentation into a single autonomous laboratory. The facility accelerates alloy development from concept to deployment, compressing timelines that traditionally exceed a decade. Scientists and engineers from universities, national laboratories, and industry use ARM-MIP as hands-on users to run discovery campaigns; automation handles sample fabrication, characterization, and routine computation while researchers focus on hypothesis generation and metallurgical decision-making. Remote access modes—including AI-guided campaign design and digital-twin-supported experiment planning—extend capabilities to researchers at non-R1 and EPSCOR institutions. Industry engages through fee-for-service, collaborative, and consortium-based arrangements. Training is delivered through on-site instruction, the Computational Materials Science Summer School (CMS3), and dedicated modules in AI-enabled discovery workflows. ARM-MIP builds a national community of practitioners across academia, industry, and national laboratories who share tools, samples, data, and knowledge.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $2.0m | 7/31/26 |