Grants and Contributions:
Title:
Convolution Neural Network enhanced Energy Accuracy Prediction in Quantum Chemistry (CEAPQC)
Agreement Number:
1029811
Agreement Value:
$33,000.00
Agreement Date:
May 1, 2025 - Mar 31, 2026
Description:
The project aims to leverage generative AI methods, including architectures that incorporate Convolution Neural Network (CNN) to find correlation between the molecular ground state energy and violations in exact conditions for the eigenstate wave-function of a molecular Hamiltonian system. The electronic structure problem is the key problem for drug design, catalyst finding, and material development. With the development of new quantum and classical computing algorithms for this problem, there is a question of how accurate the new algorithm will be for compounds of industrial relevance. These compounds are usually large molecules or periodic systems with large unit cells, so the exact solution cannot be obtained using current computing resources. The project proposes to address the question of accuracy prediction by finding transferable correlations between quantities that one can evaluate for any approximate method and whose values are known for the exact wavefunction, the latter will be referred to as indicators. To obtain reliable energy accuracy estimates, the project team will use as a training set exactly-solvable model Hamiltonians and Hamiltonians for small molecular system where the exact answer is known. The machine learning CNNs will be trained to find correlations between energy and indicator errors on a training set. It will be used to predict the accuracy of any computational method generating a wavefunction for molecules of interest.
Organization:
National Research Council Canada
Expected Results:
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Location:
Toronto, Ontario, CA M5S 1S8
Reference Number:
172-2025-2026-Q1-1029811
Agreement Type:
Grant
Report Type:
Grants and Contributions
Recipient Business Number:
108162330
Recipient Type:
Academia
Recipient's Legal Name:
The Governing Council of the University of Toronto
Federal Riding Name:
University--Rosedale
Federal Riding Number:
35112
Program:
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Purpose:
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.
NAICS Code:
541710