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Dr. Conrard G. Tetsassi Feugmo is an Assistant Professor in the Chemistry Department at the University of À¶Ý®ÊÓƵ. 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.

Dr. Feugmo’s research focuses on developing multi-scale modeling and machine learning approaches to understand and optimize high-temperature electrochemical and thermochemical systems, with particular emphasis on molten salts, solid oxide electrolyzers cells (SOECs), and corrosion in extreme environments. The group combines classical density functional theory (cDFT) with dynamic modeling to advance the fundamental understanding of many-body and non-equilibrium phenomena across states of matter, including liquids, solids, and condensed phases. Leveraging artificial intelligence (AI) and physics-informed neural networks (PINNs), the team accelerates materials discovery, enhances design strategies, and reveals the complex mechanisms driving behavior in harsh and reactive environments—ultimately bridging theoretical insight with practical applications in energy, corrosion resistance, and electrochemical technologies.

• Multi-scale modeling
• Classical density functional theory (cDFT)
• Dynamic density functional theory (DDFT)
• Physics-informed neural networks (PINNs)
• Electrochemical interface modeling
• Corrosion in molten salts
• Molten salt chemistry
• High-entropy alloys (HEAs)
• Machine learning interatomic potentials (MLIPs)
• Solid oxide electrolyzer cells (SOECs)

• 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

• 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

• Member, À¶Ý®ÊÓƵ Artificial Intelligence Institute
• Member, À¶Ý®ÊÓƵ Institute for Nanotechnology

• CHEM 120 - General Chemistry 1
  • Taught in 2025
• 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.

• View all Conrard Giresse Tetsassi Feugmo's publications on .
• 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.