Often, simulations of spectroscopic measurements such as FTIR and Raman are used to assist experimentalists to characterize samples grown in their laboratories. Although this technique works (and is widely used), there can be a high computational cost associated with the spectral simulation. This project will utilize deep neural networks 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. Collaborator University of Montréal will provide expertise in computational materials, electronic structure and first principles simulations of nanoscale structure and spectra. The NRC will provide expertise on application of AI and deep learning to problems in nanoscience.
Biographies
Dr. Michel Côté
Michel, a professor at the University of Montréal, is an expert in computational materials, electronic structure, and first principles simulations of nanoscale structure and spectra.
Michel is a principal investigator on the fast spectroscopic signatures accelerated by deep learning project.
Find out more about Dr. Côté (French only).
Dr. Li-Lin Tay
Dr. Li-Lin Tay is a Senior Research Officer and Team Leader in the NRC’s Metrology Research Centre. She has extensive expertise in the study of optical properties of low dimensional materials and surface enhanced spectroscopy as well as their applications in sensing and imaging. She leads an international research effort on Raman metrology with an aim to enable SI-traceable quantitative Raman analysis. More recently, she has worked on applying machine learning to improve accuracy and effectiveness of Raman spectral classification and spectral identifications.
Highly qualified personnel (HQP) biographies
Kevin Ryczko
Kevin is a PhD student at the University of Ottawa and a Postgraduate Affiliate at the Vector Institute for Artificial Intelligence. He is working under the supervision of Dr. Isaac Tamblyn. He specializes in utilizing deep learning techniques to accelerate the discovery and computation of materials.
Find out more about his projects at github.com/kryczko or github.com/CLEANit.
Olivier Malenfant-Thuot
Olivier is a PhD student at Université de Montréal, under the supervision of Prof. Michel Côté. They worked together on a first-principles study of graphene and the incorporation mechanisms of nitrogen atoms in its structure. He is an expert in the use of ab initio codes such as ABINIT and BigDFT. He is now working on the use of deep neural networks to conduct such studies.
His coding projects are available at github.com/OMalenfantThuot.
Arnab Majumdar
Arnab is a postdoctoral fellow who will join the project efforts in the fall of 2020. His previous experience is in electronic structure methods and its applications to various problems such as high-pressure superconductors, geophysical properties, and topological materials. He obtained his PhD at the University of Saskatchewan under the supervision of Profs. John S. Tse and Yansun Yao, and he did his first postdoctoral research with Prof. Rajeev Ahuja at Uppsala University in Sweden.
Find out more about Arnab's publications at google scholar or research gate.