Grants and Contributions

Develop digital adoption plan

Develop digital adoption plan

Develop digital adoption plan

Develop digital adoption plan

This project focuses on the investigation, development, and advancement of enabling technologies related to conformal reconfigurable intelligent surfaces and beamforming antenna technologies to enhance the performance of millimeter-wave wireless links, particularly in non-line-of?sight scenarios. These advancements encompass improvements in radiating surfaces, high efficiency phase and amplitude control for millimeter-wave integrated circuits, flexible substrates, materials, and conductive ink for printable surfaces. These efforts aim to address one of the most crucial and promising emerging wireless technologies. Additionally, the project aims to create technologies and solutions that enable broadband access, particularly in remote and rural areas.

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.

Advances exploratory research under the New Beginnings Initiative

The main objective of this research is to support decarbonisation of the construction industry by mainly moving the industry toward modular and offsite construction (MOC), carbon-less materials and implementation of circular economy to significantly reduce carbon emissions, electricity consumptions, material waste, and improve safety and productivity in MOC supply chain network. This will involve: (1) developing smart and green MOC supply chain scheduling and planning system; (2) evaluating precast concrete mixtures used in MOC to identify opportunities to reduce the embodied carbon from the materials; (3) developing BIM-based design system for manufacturing and disassembly (DfMD) supporting to design, construction, and deconstruction; and (4) developing digital twin of process and products for the carbon-less MOC supply chain network and management.

Advances exploratory research under the New Beginnings Initiative

Advances exploratory research under the New Beginnings Initiative