Grant: Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
University of Waterloo
$302,500.00
Automated material synthesis using deep reinforced learning
FY 2020-21 · 2021-03-25
Summary
This Project will develop new machine learning algorithms for automating aspects of physical chemistry by discovering new pathways for creating materials with desired properties. The goal is to use the framework of reinforcement learning as a means of discovering, controlling, and optimizing the production of new materials. This Project will explore a novel new direction to perform decision making associated with established but highly complex processes used to transform one chemical compound into another. From an algorithmic point of view, this research will tackle the challenge of working with highly nonlinear relationships and generating simulation data that is of high quality.
Department
Program
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Recipient
Location
Waterloo|Waterloo, Ontario, Canada
Recipient Type
Academia
Business Number
119260685
Type
Grant
Source
Proactive Disclosure - Grants and Contributions
Amendments
Each version of the agreement as published, oldest first, with its total as of that version. The amount above is the latest.
| Amendment | Date | Fiscal Year | Total |
|---|---|---|---|
| 0 | 2021-03-25 | 2020-21 | $302,500.00 |
| 1 | 2021-03-25 | 2020-21 | $302,500.00 |
As published
Additional Information
This agreement has been amended 1 time(s). The end date of this agreements has been modified by 398 days
Additional Information (French)
Cet accord a été modifié 1 fois. La date de fin du présent accord a été changé de 398 jours.
Agreement End Date
2024-04-30
Agreement Number
969508
Agreement Start Date
2021-03-25
Agreement Title
Automated material synthesis using deep reinforced learning
Agreement Title (French)
Synthèse automatisée de matériaux à l’aide de l’apprentissage profond par renforcement
Agreement Type
Grant
Agreement Value
$302,500.00
Amendment Date
2022-10-19
Amendment Number
1
Description
This Project will develop new machine learning algorithms for automating aspects of physical chemistry by discovering new pathways for creating materials with desired properties. The goal is to use the framework of reinforcement learning as a means of discovering, controlling, and optimizing the production of new materials. This Project will explore a novel new direction to perform decision making associated with established but highly complex processes used to transform one chemical compound into another. From an algorithmic point of view, this research will tackle the challenge of working with highly nonlinear relationships and generating simulation data that is of high quality.
Description (French)
Ce projet vise à mettre au point de nouveaux algorithmes d’apprentissage automatique pour automatiser certains aspects de la chimie physique grâce à la découverte de nouveaux processus pour la création de matériaux ayant des propriétés particulières. L’objectif est d’utiliser le cadre de l’apprentissage par renforcement pour découvrir de nouveaux matériaux ainsi que contrôler et optimiser leur production. Le présent projet explorera une nouvelle stratégie de prise de décision associée à des processus établis mais très complexes utilisés pour transformer un produit chimique en un autre. D’un point de vue algorithmique, ces travaux de recherche relèveront le défi de travailler avec des relations hautement non linéaires et de générer des simulations de haute qualité.
Expected Results
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.
Federal Riding
Waterloo
Federal Riding (French)
Waterloo
Federal Riding Number
35112
NAICS Code
541710
Organization Code
nrc-cnrc
Organization
National Research Council Canada | Conseil national de recherches Canada
Program Name
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
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.
Recipient Business Number
119260685
Recipient City
Waterloo|Waterloo
Recipient Country
CA
Recipient Legal Name
University of Waterloo
Recipient Postal Code
N2L 3G1
Recipient Province or Territory
ON
Recipient Type
S
Reference Number
172-2020-2021-Q4-969508