The design of new functional materials has been greatly assisted by developments in efficient and accurate electronic-structure methodologies, most notably density functional theory (DFT). However, DFT is too expensive to perform the rapid screening of thousands of candidates required for the design of new high-performance materials. This project aims to harness the power of modern artificial intelligence to build on the success of DFT and ferret out optimal design parameters. This research is of prime socioeconomic importance, aiming to reduce the environmental footprint of the Alberta oil sands by designing new nanocatalysts for the chemical reactions involved in in-the-reservoir upgrading. Collaborator University of Calgary brings advanced knowledge of theoretical and computational-based nanocatalyst discovery to the project while the National Research Council brings expertise in statistical signal processing and machine learning.
Project team
Dr. Dennis Salahub
Dr. Dennis Salahub is Professor Emeritus, Department of Chemistry at the University of Calgary. His research interests are in theoretical and computational chemistry, especially density functional theory (DFT) and its applications in materials and biomolecular modelling.
Find out more about Dr. Salahub.
Dr. Alain Tchagang
Dr. Alain Tchagang is a research officer with the National Research Council. He has a wealth of research experience combining state-of-the-art methods in statistical signal processing and machine learning to tackle novel and challenging problems in life science, physics and engineering.
Find out more about Dr. Tchagang and his other projects.