Grants and Contributions:

Graph Representation Learning with Limited Labeled Data for Property Prediction in Design

964159

$199,980.00

Dec 21, 2020 - Jan 1, 2023

Deep learning models are increasingly being adopted and rapidly improved for property prediction in intelligent design, including predicting the functionalities of a novel circuit, foreseeing the working behaviors of a new material, and anticipating the reactions of a fresh compound. State-of-the-art deep techniques leverage two building blocks: deep graph neural networks and supervised signals from large amount of labelled data. Graphs are powerful and versatile data structures to model the relationships among objects, and powered by the graph structure, supervised learning leverages large amount of labelled data to construct efficient graph representation, which then enables the prediction model to make accurate decisions on the properties of a new design. Such state-of-the-art graph neural networks for property prediction, rely heavily on supervised learning with large labelled datasets, which is typically expensive and time-consuming. This Project will examine the development of fundamental techniques for graph representation learning in the presence of limited labeled data, aiming at accurate property prediction of novel designs. The successful outcome of this Project will be a learning framework that enables the efficient learning of graph representation with limited labelled data. Consequently, with improved graph representation, the properties of a new design can be more accurately and effectively evaluated, resulting in meaningfully speeding up the search for desirable designs.

National Research Council Canada

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.

Montreal, Quebec, CA H3T 2A7

172-2020-2021-Q3-964159

Grant

Grants and Contributions

107278905

Academia

HEC Montreal

Outremont

24054

Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives

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.

541745