Grants and Contributions
About this information
In June 2016, as part of the Open Government Action Plan, the Treasury Board of Canada Secretariat (TBS) committed to increasing the transparency and usefulness of grants and contribution data and subsequently launched the Guidelines on the Reporting of Grants and Contributions Awards, effective April 1, 2018.
The rules and principles governing government grants and contributions are outlined in the Treasury Board Policy on Transfer Payments. Transfer payments are transfers of money, goods, services or assets made from an appropriation to individuals, organizations or other levels of government, without the federal government directly receiving goods or services in return, but which may require the recipient to provide a report or other information subsequent to receiving payment. These expenditures are reported in the Public Accounts of Canada. The major types of transfer payments are grants, contributions and \'other transfer payments\'.
Included in this category, but not to be reported under proactive disclosure of awards, are (1) transfers to other levels of government such as Equalization payments as well as Canada Health and Social Transfer payments. (2) Grants and contributions reallocated or otherwise redistributed by the recipient to third parties; and (3) information that would normally be withheld under the Access to Information Act and the Privacy Act.
$64,825.00
May 10, 2017
Combinatorial Games and Graph Optimization: Losing and Scoring, Packing and Walking
RGPIN
$115,000.00
May 10, 2017
Improvising with the dead
RGPIN
$24,997.00
Jul 12, 2017
Determine Optimal Light Intensities for Indoor Medical Cannabis Production
EGP
$165,225.62
Apr 1, 2021
Academia
Realizing the potential of atrificial intelligence/machine learning (AI/ML) to guide experimental discovery of acid-stable and platinum group metal-free (PGM-free) oxygen evolution reaction (OER) catalysts
971729
The Project is a collaboration between Carnegie Mellon University, the University of Toronto, and the National Research Council to develop and apply data-driven and machine learning methods for the experimental discovery of new acid-stable materials promoting the oxygen evolution reaction. Specifically, the collaborative Project will seek to develop the necessary datasets and methods to investigate an important class of candidate materials: mixed metal oxides. There will be investigation of generative models to improve the relevance of these and to accelerate experimental testing methods. This will require experimental synthesis, characterization, and testing of a small number of candidate materials.
Carnegie Mellon University will screen bimetallic or multicomponent oxide materials using high-throughput calculations. It will also develop and apply graph convolution models to predict stability under reaction conditions. Materials that are predicted to be the most active will be selected for further detailed calculations on potential activity, and provided to experimental collaborators at the University of Toronto.
$473,775.00
Apr 1, 2021
Academia
Realizing the potential of atrificial intelligence/machine learning (AI/ML) to guide experimental discovery of acid-stable and platinum group metal-free (PGM-free) oxygen evolution reaction (OER) catalysts
970540
The Project will develop and apply data-driven and machine learning methods to the experimental discovery of new acid-stable materials for the oxygen evolution reaction. The Project comprises the University of Toronto portion of a three-way collaboration between the National Research Council, the University of Toronto, and Carnegie Mellon University. Overall, the collaborative Project will seek to develop the necessary datasets and methods needed to investigate an important class of candidate materials: mixed metal oxides. Additionally, the Project will investigate generative models to improve the relevance of identified candidates and to accelerate the experimental testing methods of these as well their synthesis and characterization. The University of Toronto will optimize the synthesis methodology and proceed to synthesize and fully characterize a number of candidate materials identified using the accelerated testing methods developed.
$150,000.00
May 10, 2017
Mechanisms underlying the control and generalization of sensorimotor learning
RGPIN
$130,000.00
May 10, 2017
Extending Everyday Practices in the Home with Connected Things
RGPIN
$115,000.00
May 10, 2017
Methods and Tools for Active Adapation in Serious Games Based on a Rich User Model
RGPIN
$165,000.00
May 10, 2017
Improved Autonomy for Unmanned Aerial Vehicles in Unstructured Environments
RGPIN
$115,000.00
May 10, 2017
Pattern and Knowledge Discovery on Relational, Biosequence and Multiple Temporal Sequence Data
RGPIN