Conrard Giresse Tetsassi Feugmo

Dr. Conrard G. Tetsassi Feugmo is an Assistant Professor in the Department of Chemistry at the University of Waterloo. He holds a master’s degree in Nanotechnologies from the Louvain School of Engineering and a Ph.D. in Computational Chemistry from the University of Namur, both in Belgium.

Dr. Feugmo’s research program at Waterloo focuses on developing multiscale modeling and machine learning frameworks to understand and optimize high-temperature electrochemical and thermochemical systems. His group investigates molten salts, solid oxide electrolysis cells (SOECs), solid-state batteries, and corrosion in extreme environments, combining classical density functional theory (cDFT), molecular dynamics (MD), density functional theory (DFT), and data-driven methods to uncover the many-body and non-equilibrium phenomena governing materials performance.

A central theme of his work is the integration of Physics-Informed Neural Networks (PINNs) and Machine learning Potential to the modeling of electrochemical interface and predictive, interpretable, and computationally efficient models for energy materials. By embedding fundamental physical laws—such as the Butler–Volmer, Nernst, and diffusion equations—into neural network architectures, his team develops Electrochemistry-Informed Neural Networks capable of directly inferring kinetic and transport parameters from experimental data. This approach allows accurate modeling of processes such as charge transfer, mass transport, and localized corrosion, providing deep insight into the electrochemical interface that dictates material lifetime and energy efficiency.

Another growing area of the group’s research explores the design and behavior of high-entropy alloys (HEAs)—a new class of compositionally complex materials exhibiting exceptional mechanical strength, thermal stability, and corrosion resistance in harsh electrochemical and high-temperature environments. By leveraging machine learning and physics-based simulations, the team aims to elucidate the underlying atomic-scale mechanisms that make HEAs promising candidates for structural and functional applications in energy and nuclear 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)

Dr. Feugmo’s research program at Waterloo focuses on developing multiscale modeling and machine learning frameworks to understand and optimize high-temperature electrochemical and thermochemical systems. His group investigates molten salts, solid oxide electrolysis cells (SOECs), solid-state batteries, and corrosion in extreme environments, combining classical density functional theory (cDFT), molecular dynamics (MD), density functional theory (DFT), and data-driven methods to uncover the many-body and non-equilibrium phenomena governing materials performance. A growing area of the group’s research explores the design and behavior of high-entropy alloys (HEAs)—a new class of compositionally complex materials exhibiting exceptional mechanical strength, thermal stability, and corrosion resistance in harsh electrochemical and high-temperature environments. By leveraging machine learning and physics-based simulations, the team aims to elucidate the underlying atomic-scale mechanisms that make HEAs promising candidates for structural and functional applications in energy and nuclear technologies.

A central theme of his work is the integration of Physics-Informed Neural Networks (PINNs) and Machine learning Potential to the modeling of electrochemical interface and predictive, interpretable, and computationally efficient models for energy materials. By embedding fundamental physical laws—such as the Butler–Volmer, Nernst, and diffusion equations—into neural network architectures, his team develops Electrochemistry-Informed Neural Networks capable of directly inferring kinetic and transport parameters from experimental data. This approach allows accurate modeling of processes such as charge transfer, mass transport, and localized corrosion, providing deep insight into the electrochemical interface that dictates material lifetime and energy efficiency.

Over the next decade, Dr. Feugmo aims to establish these AI-augmented, physics-grounded models as a foundation for predictive design and lifetime assessment of advanced energy materials. His long-term vision is to unify quantum-to-continuum modeling, machine learning, and experimental data into accountable, generalizable computational tools supporting technologies critical to a sustainable future—including corrosion-resistant alloys, clean energy and molten salt reactor systems.

Dr. Feugmo is collaborating with Vianode Canada, an advanced battery materials company, on a project supported by Mitacs Accelerate program to advance the development of fast-charging lithium-ion batteries. This initiative strengthens R&D at Vianode’s large-scale Via TWO facility in St. Thomas, Ontario, which produces low-emission synthetic anode graphite for electric vehicles. At the University of Waterloo, Dr. Feugmo’s team is modeling lithium-ion transport mechanisms within graphite to improve charging speed—one of the most critical performance factors for next-generation EV batteries. This collaboration is driving innovation in fast-charging materials, reducing development costs.

• 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, Waterloo Artificial Intelligence Institute

• Member, Waterloo 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, 2025

* Only courses taught in the past 5 years are displayed.

• 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.