When AI, Modeling, and self-driving laboratories Harmonize for Advanced Material Discovery
Nov 17, 2023
2:30PM to 3:30PM
Date/Time
Date(s) - 17/11/2023
2:30 pm - 3:30 pm
Categories
Prof. Conrard Giresse Tetsassi Feugmo
Dept. of Chemistry, University of Waterloo
In the ever-evolving field of materials science, the pursuit of a comprehensive understanding, prediction, and enhancement of materials properties remains at the forefront. In response to the prohibitive costs associated with traditional trial-and-error methods in materials research, characterized by repetitive cycles of material synthesis and characterization, researchers are increasingly turning to innovative approaches. These include the utilization of modeling techniques, Artificial Intelligence, and robotic platforms, not only to gain deeper insights into materials properties beforehand but also to streamline research and development processes. This shift aims to reduce both R&D timelines and material/processing expenses while expediting the scale-up process to drive overall technological advancements. Central to this endeavor is the quest for the perfect synergy among these three critical components: modeling, AI, and robotics, in order to establish a closed loop for autonomous materials discovery and development. Herein, we explore the challenges and opportunities in AI-driven materials discovery and delve into the interconnected roles of the Reader, the Maker, the Predictor, the Designer, and the Storage, all essential elements for achieving this transformative loop closure.
Bio:
Dr. Conrard Giresse Tetsassi Feugmo is currently serving as an assistant professor in chemistry at the University of Waterloo. He holds both a B.Sc. and M.Sc. in Chemistry from the University of Yaoundé I in Cameroon. Continuing his academic journey, he pursued further studies in Belgium, where he achieved a Specialized Master’s degree in Nanotechnologies from the Louvain School of Engineering, followed by a Ph.D. in Computational Chemistry from the University of Namur. After completing his Ph.D., Dr. Feugmo gained valuable experience as a postdoctoral associate at the University of Western Ontario. Prior to joining the University of Waterloo, he worked as a Research Officer at the NRC’s Advanced Materials Research Facility in Mississauga, utilizing his expertise in material science, nanotechnologies, and machine learning to develop a materials acceleration platform focused on sustainability-related domains.
Dr. Tetsassi Feugmo’s research interests span diverse areas, with a primary focus on expediting the design process of high entropy alloys (HEA) and mixed metal oxides (MMO) using computational approaches such as Density Functional Theory (DFT), Molecular Dynamics (MD), Phase Field Crystal (PFC), and machine learning. By utilizing these methodologies, he is able to delve into the microstructure properties, driving significant progress in energy storage and conversion technologies including fuel cells, batteries, hydrogen technology, and capacitors. Furthermore, he actively engages in designing HEA materials for applications in the hydrogen storage, aeronautics, and nuclear industries. Additionally, he explores the development of hybrid gas sensors that amalgamate organic and metal oxide materials, enabling effective detection of volatile organic compounds. To optimize material efficiency and performance, he is dedicated to the development of constrained multi-objective optimization algorithms and a model for ionic conduction in non-crystalline materials. These tools empower to fine-tune material properties and functionality, ultimately contributing to the advancement of energy storage and conversion systems.
In-Person: ABB 102
Online: https://mcmaster.zoom.us/j/94130132589
Meeting ID: 941 3013 2589
Passcode: 007141