Reference number : 172-2023-2024-Q4-1015247, amendment number : , agreement type : G, recipient legal name : The Governing Council of the University of Toronto, recipient country : CA, recipient province : ON, recipient city : Toronto|Toronto, recipient postal code : M5G 1L5, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : 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., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Artificial intelligence for battery material property-performance predictions and battery remaining useful lifetime, agreement title french : Intelligence artificielle pour la prévision des propriétés et de la performance des matériaux de batterie et de la durée de vie utile restante de la batterie, agreement number : 1015247, agreement value : 496650.0, agreement start date : 2024-03-26, agreement end date : 2027-03-31, description english : This project will develop a physics informed machine learning approach to predicting battery durability to be implemented into a self-driving laboratory being developed by the NRC. It leverages University of Toronto AI-assisted tools for fitting electrochemical impedance spectroscopy (EIS), a central tool in measuring electrochemical systems, and generating statistically significant and unbiased models of physical processes. The Recipient will refine and apply this tool to support the NRC’s development of novel battery cathodes by developing (1) an automated sensitivity analysis and out of distribution detection algorithm to enable model updating during active learning studies, (2) a robust modeling framework for generating physical insights into battery performance and degradation, which will permit scientifically informed adjustments to battery formulations, and (3) an active learning tool that combines these tools to predict battery longevity without long term cycling studies. All data and code generated will be released publicly to benefit all Canadians., description french : Intelligence artificielle pour la prévision des propriétés et de la performance des matériaux de batterie et de la durée de vie utile restante de la batterie, expected results english : 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., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : nan, additional information french : nan, owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada