This National Science Foundation (NSF) Project Grant award, under the Mathematical and Physical Sciences program (CFDA 47.049), supports theoretical and computational research to enhance the accuracy and efficiency of first-principles quantum mechanical simulations for understanding the electronic structure of materials. The $220,991 award will fund the development of innovative machine learning-based approximations to improve the Density Functional Theory (DFT) methodology, which is widely used...
The National Science Foundation (NSF) awarded a $420,000 Project Grant through the Mathematical and Physical Sciences (CFDA 47.049) program to Northeastern University. The 3-year grant, beginning August 1, 2024, will fund research focused on developing novel machine learning-inspired computational methods and advancing the understanding of quantum many-body systems. Key efforts include: 1) applying neural network models based on "quantum attention" to study quantum many-body ground...
The National Science Foundation (NSF) awarded a $1,149,999 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program to the University of Massachusetts (UMass) Amherst. This 3-year grant, effective July 1, 2024, will fund the development of an open-source ecosystem of modeling tools for quantum hardware. The project aims to democratize access to quantum technology by creating user-friendly tools that allow researchers to model quantum systems...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $331,902 Project Grant to the Trustees of Boston University on August 15, 2023 under the Mathematical and Physical Sciences program (CFDA 47.049). The purpose of this 3-year grant is to develop rigorous mathematical analysis and theory for the training algorithms used in neural network models across various machine learning applications. The research will leverage stochastic analysis and weak convergence theory...
This $340,000 project grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports theoretical and computational research to achieve a fundamental understanding of the electronic and transport properties of condensed matter systems. The University of Massachusetts (UMass), through its Office of Grant & Contract Administration, will employ advanced analytical methods like the worm algorithm and diagrammatic Monte Carlo to study...
This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) Project Grant award of $256,892 to the University of Alabama at Birmingham (UAB) aims to develop new computational methods to simulate the fundamental behaviors and dynamics of electrons at the quantum level by incorporating machine learning techniques. The project, led by an assistant professor at UAB, involves collaborating with an expert at the University of Texas at Austin to create a machine-learning-based,...
This $250,000 National Science Foundation project grant will support research at the University of Massachusetts Boston and the University of California-Santa Barbara to advance machine learning techniques for predicting the behavior of dynamic materials. Jason Green of UMass Boston and Igor Mezic of UC Santa Barbara will combine machine learning and physical theory methods to create new approaches for designing functional materials with tailored optical, mechanical, or photonic properties on...
This $700,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will fund the development of machine learning tools and quantum-classical hybrid algorithms to advance quantum materials research and optimize the use of noisy intermediate-scale quantum devices. Led by Cornell University with subawards to Princeton University, New York University, and Harvard University, the project will deliver new topological materials, a...
This $1,065,000 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support collaborative research by the University of Pennsylvania, University of Wisconsin-Madison, and Northeastern University to develop novel quantum materials through deep learning-guided "twistronics". The researchers will use computer-aided deep learning models and theoretical tools to predict and guide the self-assembly of two-dimensional...
The National Science Foundation (NSF) awarded a $277,686 Project Grant under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to the Regents of the University of Minnesota. The grant, titled "COLLABORATIVE RESEARCH: NSF-NSERC: DATA-ENABLED MODEL ORDER REDUCTION FOR 2D QUANTUM MATERIALS", will develop advanced computational modeling workflows that merge quantum modeling and machine learning methods. This will enable rapid, automated, high-fidelity exploration...