Conrard Giresse Tetsassi Feugmo
Biography
Dr. Conrard G. Tetsassi Feugmo is an Assistant Professor in the Chemistry Department at the University of Waterloo. He holds a Master’s in Nanotechnologies from the Louvain School of Engineering in Belgium and a Ph.D. in Computational Chemistry from the University of Namur, Belgium.
The research group led by Dr. Feugmo is dedicated to advancing scientific methodologies for exploring condensed matter at both atomic and microscale levels. By expertly integrating machine learning with mathematical techniques, the group achieves precise computation and a deeper understanding of complex physical properties and the dynamic behavior of many-body systems, especially under non-equilibrium conditions. The group’s work is grounded in computational techniques such as Quantum and Classical Density Functional Theory (DFT), Molecular Dynamics (MD), and Artificial Intelligence (AI). These approaches are applied to developing advanced materials for energy applications, including molten salt reactors (MSR), hydrogen technology, and solid oxide electrolysis cell (SOEC). Much of the research also focuses on diffusion in solid systems and the application of automatic differentiation.
Before his current role, Dr. Feugmo was a Research Officer at the National Research Council’s Advanced Materials Research Facility in Mississauga. His extensive experience in computational chemistry and materials science is driven by a commitment to addressing global energy challenges.
The research group led by Dr. Feugmo is dedicated to advancing scientific methodologies for exploring condensed matter at both atomic and microscale levels. By expertly integrating machine learning with mathematical techniques, the group achieves precise computation and a deeper understanding of complex physical properties and the dynamic behavior of many-body systems, especially under non-equilibrium conditions. The group’s work is grounded in computational techniques such as Quantum and Classical Density Functional Theory (DFT), Molecular Dynamics (MD), and Artificial Intelligence (AI). These approaches are applied to developing advanced materials for energy applications, including molten salt reactors (MSR), hydrogen technology, and solid oxide electrolysis cell (SOEC). Much of the research also focuses on diffusion in solid systems and the application of automatic differentiation.
Before his current role, Dr. Feugmo was a Research Officer at the National Research Council’s Advanced Materials Research Facility in Mississauga. His extensive experience in computational chemistry and materials science is driven by a commitment to addressing global energy challenges.
Research Interests
- Computational Chemistry
- Artificial Intelligence for Materials Science
- Classical dynamical density functional theory (DDFT)
- Many-Body Dynamics - Non-equilibrium System
- Solid-State Diffusion
- Automatic Differentiation
- Small Modular Reactors (SMRs)
- Hydrogen Technology
- Solid oxide electrolysis cell
- Materials Degradation
Education
- 2018, PhD, Computational Chemistry, University of Namur, Belgium
- 2011, MSc, Nanotechnologies, Louvain School of Engineering, Belgium
- 2010, MSc, Chemistry, University of Yaounde I, Cameroon
- 2007, BSc, Chemistry, University of Yaounde I, Cameroon
Awards
- 2022, Talent Bursaries Alberta AI-week
- 2019, Center for Nonlinear Studies at Los Alamos National Laboratory travel grants
- 2018, Western’s Postdoctoral Fellowships Program
- 2014, C.G.B. (Comité de Gestion du Bulletin) - C.B.B travel grants
- 2014, Gordon Research Conferences travel grants
- 2012, Institutional PhD CERUNA grants
- 2011, Special Research Fund (FSR) Scholarship
Affiliations and Volunteer Work
- Member, Waterloo Artificial Intelligence Institute
- Member, Waterloo Institute for Nanotechnology
Teaching*
- CHEM 123 - General Chemistry 2
- Taught in 2023, 2024
- NE 451 - Simulation Methods
- Taught in 2023, 2024
* Only courses taught in the past 5 years are displayed.
Selected/Recent Publications
- View all Conrard Giresse Tetsassi Feugmo's publications on Google Scholar.
- Zachary Gariepy, ZhiWen Chen, Isaac Tamblyn, Chandra Veer Singh, Conrard Giresse Tetsassi Feugmo; Automatic graph representation algorithm for heterogeneous catalysis. APL Mach. Learn. 1 September 2023; 1 (3): 036103.
- Tetsassi Feugmo, C.G. Accurately predicting molecular spectra with deep learning. Nat Comput Sci 3, 918–919 (2023)
- Zhi Wen Chen, Zachary Gariepy, Lixin Chen, Xue Yao, Abu Anand, Szu-Jia Liu, Conrard Giresse Tetsassi Feugmo, Isaac Tamblyn, and Chandra Veer Singh; Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO2 Reduction Reaction ACS Catalysis 2022 12 (24), 14864-14871
- Tetsassi Feugmo CG, Ryczko K, Anand A, Singh CV, Tamblyn I. Neural evolution structure generation: High entropy alloys. J Chem Phys. 2021, 155, 044102.
- Federizon J, Tetsassi Feugmo CG, Huang WC, He X, Miura K, Razi A, Ortega J, Karttunen M, Lovell JF. Experimental and Computational Observations of Immunogenic Cobalt Porphyrin Lipid Bilayers: Nanodomain-Enhanced Antigen Association. Pharmaceutics. 2021, 13, 98.
- Tetsassi Feugmo CG, Liégeois V, Caudano Y, Cecchet F, Champagne B. Probing alkylsilane molecular structure on amorphous silica surfaces by sum frequency generation vibrational spectroscopy: First-principles calculations. J Chem Phys. 2019,150,074703.