Reference number : 172-2023-2024-Q4-1014941, amendment number : , agreement type : G, recipient legal name : University of Ottawa, recipient country : CA, recipient province : ON, recipient city : Ottawa|Ottawa, recipient postal code : K1N 6N5, 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 : Deep Generative AI for the Design and Synthesis of Perovskite with, agreement title french : IA générative utilisant l’apprentissage profond pour la conception et la synthèse de pérovskite avec propriétés ciblées, agreement number : 1014941, agreement value : 258500.0, agreement start date : 2024-03-21, agreement end date : 2026-03-31, description english : The field of novel materials discovery has witnessed several interesting techniques that span across traditional synthetic chemistry and computational methods. Synthetic chemistry methods are normally Edisonian-based (i.e., trial-and-error) and often will rely on experiential knowledge to synthesize chemically stable materials. Machine Learning (ML) approaches, that are based on Deep Generative Modeling (DGM), are
commendable alternatives for novel materials discovery due to their impressive ability in analyzing robust chemical design space. Famous for their speed, reliability, and low cost, DGM techniques can intelligently identify hidden patterns and correlations in a training dataset by solving an inverse design scheme. However, deep generative ML (DGML) approaches face
challenges related to lattice reconstruction at the decoding phase, potentially leading to two major shortcomings.
To address the highlighted challenges, the project will develop progressive deep learning approaches for novel materials discovery in two stages. The first stage will leverage on the technical strengths of both a semi-supervisory VAE (i.e. SS-VAE) model and an auxiliary GAN (i.e. A-GAN) model. The model architecture is referred to as Lattice-Constrained Materials Generative
Model (LCMGM) and will be used to screen stable inorganic perovskite materials with crystal lattice conformities that are consistent with predefined
symmetrical constraints at the encoding phase. Furthermore, the second stage will involve the novel design and development of a Thermodynamics
Guided Diffusive Generative Model (TGDGM) for discovering Hybrid Organic-Inorganic Perovskite (HOIP). The TGDGM shall build on the LCMGM for facilitating autonomous materials search with high scalability and
reliability.
The AI generated perovskite crystal structures will be synthesized using highly-scalable experimental means for meeting targeted applications in many engineering domains. The synthetization processes will be developed
for the most promising AI discovered compounds at the University of Ottawa.
The focus will be on producing powders or particles with optimized properties for future research on novel perovskite structures produced by coating or layering. Two approaches will be investigated simultaneously, starting primarily from mixed oxide particles: (1) solid state processing and (2) sol gel synthesis. In addition, extensive materials characterization will provide
important knowledge and materials data, which will serve as crucial closeloop feedback for further optimizations of both the Machine Learning models and synthetization processes, with primary focus on applications such as
solar cells, energy storage materials and catalysts., description french : IA générative utilisant l’apprentissage profond pour la conception et la synthèse de pérovskite avec propriétés ciblées, 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