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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Arizona State University |
| Country | United States |
| Start Date | Dec 01, 2024 |
| End Date | Nov 30, 2025 |
| Duration | 364 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2437244 |
The broader impact/commercial potential of this I-Corps project is based on the development of an innovative software package designed for the discovery of new materials aimed at advancing sustainability efforts. This technology leverages state-of-the-art quantum computing to identify materials that can address a wide range of sustainability issues, such as carbon dioxide capture, hydrogen transport and storage, and the development of new semiconductors for solar energy conversion or next-generation chips.
The software integrates quantum machine learning for initial material screening and quantum chemistry simulations for detailed analysis, significantly accelerating the discovery process compared to traditional methods. This solution has broad commercial applications across multiple sectors, including environmental sustainability, energy, and advanced materials, supporting global efforts to mitigate climate change and reduce carbon emissions.
This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of the proposed technology. It is based on the prior development of an innovative software package designed for the discovery of new materials aimed at advancing sustainability efforts. This solution utilizes two primary functionalities: quantum machine learning and quantum chemistry simulations.
The quantum machine learning component utilizes quantum-enhanced artificial intelligence algorithms for the initial screening of potential materials, efficiently navigating the vast material design space. The quantum chemistry simulations component performs detailed analysis of promising materials identified during the initial screening, calculating crucial material descriptors such as binding energies and diffusion coefficients.
This dual approach allows for rapid and accurate assessment of material candidates for sustainable technologies.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Arizona State University
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