Grants and Contributions:

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Title:

FAST SPECTROSCOPIC SIGNATURES ACCELERATED BY DEEP LEARNING

Agreement Number:

948094

Agreement Value:

$160,600.00

Agreement Date:

Apr 16, 2020 - Mar 31, 2022

Description:

FTIR and Raman are common spectroscopic tools used in materials synthesis and characterization. Deep neural networks will be used to develop an efficient and accurate methodology for simulating accurate spectroscopic signatures at significantly lowered computation cost than currently possible. This study will focus on 2D materials, beginning with graphene. AI will be used to: 1. Produce approximate (yet accurate) spectra from atomic structures using supervised learning on a large dataset of first principles-based calculations. The deep neural network will interpolate those spectra at a fraction of the original cost of generating them. The target is to develop an AI model which allows simulation of spectra from much larger (and therefore complex) structures than currently possible. 2. Searching the space of possible structures to find a simulated spectra which best matches experimental signal. Genetic algorithms will be explored, where the objective function will be to minimize the KL divergence between the spectra produced by the neural network and the experimental signal.

Organization:

National Research Council Canada

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.

Location:

Montreal, Quebec, CA H3T 1J4

Reference Number:

172-2020-2021-Q1-948094

Agreement Type:

Grant

Report Type:

Grants and Contributions

Recipient Business Number:

108160995

Recipient Type:

Academia

Recipient's Legal Name:

Université de Montréal

Federal Riding Name:

Outremont

Federal Riding Number:

24054

Program:

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

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.

NAICS Code:

541710 - R&D in the physical, engineering and life sciences