Project Grant 2608825
- This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $491,583 to the Texas A&M Engineering Experiment Station (Tees) to develop a comprehensive education and hands-on training program that integrates computational materials science (CMS), machine learning/artificial intelligence techniques, and accelerated high-performance computing. The program aims to equip the future workforce with cutting-edge skills to drive innovation in...
- This $2,250,000 Project Grant award from the Office of Advanced Cyberinfrastructure within the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program seeks to develop a software platform and infrastructure for training machine learning (ML) models at scale to predict the properties of molecular and materials systems. The key objectives are to: Establish a technological paradigm and software infrastructure for developing ML models capable of predicting...
- The National Science Foundation Division of Computing and Communication Foundations awarded Texas A&M Engineering Experiment Station $305,483 on September 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) for collaborative research on advanced diffusion models for molecular graph generation. The project develops generative artificial intelligence methods to accelerate molecular discovery by generating chemically meaningful molecular structures under...
- This $359,790 Project Grant awarded by the National Science Foundation (NSF) Division of Materials Research supports theoretical and computational research at the University of Central Florida (UCF) aimed at advancing the fundamental understanding of solid-solid phase transitions. The research seeks to identify the physical principles and structural motifs controlling the kinetics of these phase transitions, and to develop a machine learning method for rapid and accurate prediction of transition...
- The National Science Foundation awarded $304,905 to Tailwater Informatics Holdings, Corp. on August 15, 2026, under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) to develop an AI-driven cloud application programming interface for quantum-accurate materials simulation. The project develops a foundational AI model designed to predict electronic structure of solid materials with high-fidelity accuracy in seconds, addressing a computational bottleneck in materials science...
- 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...
- Federal Grant Award Summary Texas A&M Engineering Experiment Station received a $1.6 million collaborative research Project Grant from the National Science Foundation's Division of Materials Research under the Mathematical and Physical Sciences program (CFDA 47.049), effective October 1, 2025, through September 30, 2029. This award supports accelerated development of polymer salogels—composite materials combining star-shaped block copolymers with inorganic salt hydrates—to create advanced...
- This National Science Foundation (NSF) Project Grant award for $333,028 under the NSF Computer and Information Science and Engineering (CISE) program (CFDA 47.070) funds a collaborative research initiative led by Rutgers, The State University of New Jersey. The project aims to address the challenge of training students, researchers, and instructors in computational tools and techniques at the chemistry, physics, and materials science interface. The four-year project will establish a robust...
- Federal Grant Award Summary The National Science Foundation's Office of Advanced Cyberinfrastructure awarded a $680,000 Project Grant to the California Institute of Technology (Caltech) under the Computer and Information Science and Engineering program (CFDA 47.070) beginning September 1, 2025 and concluding August 31, 2030. This collaborative research initiative focuses on building next-generation infrastructure for the PySCF (Python Simulations of Chemistry Framework) software platform to...
- This National Science Foundation Project Grant of $533,432 awarded on March 1, 2023 will fund research, education, and outreach activities at Texas A&M Engineering Experiment Station through February 28, 2028. Under the NSF Engineering program (CFDA 47.041), this award will support the development of an all-scale continuum modeling framework to predict plastic deformation and failure across material scales. The awardee will conduct research on magnesium-aluminum micropillars, nanoporous...
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 platform extending beyond traditional metallurgy into ceramics, semiconductors, functional oxides, and battery chemistries. The platform combines machine learning with thermodynamic theory to enable academic researchers, national laboratories, and industry users to build high-fidelity databases for advanced manufacturing, energy, aerospace, microelectronics, and defense applications. The computational tools created through this award feed simulation, machine learning, and autonomous experimentation workflows that compress materials-development cycles. PhaseForge includes a computational materials science summer school to expand access for institutions without commercial tools, strengthening cyberinfrastructure for materials discovery and training the next generation of computational materials scientists. The award period runs through July 31, 2029, with performance at College Station, Texas. The work is classified as a Project Grant.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $299.8k | 7/28/26 |