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Completed STANDARD GRANT National Science Foundation (US)

I-Corps: Highly-Accurate Chemical Simulations for the Energy Storage Industry

$500K USD

Funder National Science Foundation (US)
Recipient Organization University of Arizona
Country United States
Start Date Apr 01, 2021
End Date Sep 30, 2022
Duration 547 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2114713
Grant Description

The broader impacts/commercial potential of this I-Corps project is to improve design and processes in industries such as energy storage, battery technology, quantum computing, memory storage, and pharmaceuticals, as well as military defense, through simulation. Simulating chemical processes with computer software is vital for improved designs and processes in industries ranging from energy storage to pharmaceuticals.

The accurate verification, refinement, and prediction of states of matter is particularly advantageous for improving the design and manufacturing of new and renewable materials. Using quantum information in highly accurate simulations will disrupt these industries and become the new standard in chemical computational software.

This I-Corps project aims to solve out-of-reach energy and chemistry problems with highly-accurate simulations. Simulating chemical processes with computer software is vital for improved designs and processes in industries ranging from energy storage to pharmaceuticals. Highly-accurate simulations have been uniquely limited in the prediction of states due to the extensively adopted and utilized Born-Oppenheimer approximation (BOA) of the 1920s.

The core of the innovation does not assume the historically necessary BOA and exploits the effectiveness and predictive power of all-particle correlation by using explicitly correlated Gaussian functions in conjunction with the well-known Rayleigh-Ritz variational theorem to describe chemical phenomena. The proposed technology leverages specialized basis functions that include non-zero angular momentum state functions through the use of spherical harmonics, including complex parameters.

The proposed QLEAN™ (Quantum Learned Electrons and Nuclei) simulation method provides a scaling solution that uniquely reduces the exponential growth rate dependence on the number (n) of identical particles, also known as the factorial dependence, to a factor of n-squared, making quantum based simulations possible. The I-Corps program will enable the team to discover the market interest in this simulations program.

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.

All Grantees

University of Arizona

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