Conrard Giresse Tetsassi Feugmo

Conrard Giresse Tetsassi Feugmo is an Assistant Professor in the Department of Chemistry at the University of Waterloo, where he has led a research group since 2022.

He studied chemistry at the Université de Yaoundé I in Cameroon, taking a BSc in 2007 and an MSc in 2010, then moved to Belgium for an M.Phil. in nanotechnologies at UCLouvain and a PhD in computational chemistry at the University of Namur, awarded in 2018. He came to Canada as a postdoctoral associate at Western University and was a research officer at the National Research Council of Canada before joining Waterloo.

His group works on how materials degrade and fail in demanding electrochemical environments: the molten salt coolants proposed for advanced nuclear reactors, the interfaces that set the lifetime of batteries and fuel cells, the oxide films that protect reactor piping. The common difficulty is that the governing equations are known but cannot be solved at the scales where measurements are made, so the field substitutes empirical fits that carry no mechanism and fail outside the conditions they were calibrated in.

His response is to write the physics as differentiable programs, so that one piece of code can predict forwards, recover mechanism backwards from sparse experimental data, and hand back a design. Machine learning enters as a way to solve and invert the governing equations rather than to replace them. Embedding those equations in the model keeps it thermodynamically consistent, makes its parameters mean something, and removes the need for the large labelled datasets that materials problems rarely have.

He is as interested in where the approach fails as in where it works. Much of his published work reports a boundary rather than a capability: which parameters a corrosion measurement can and cannot determine, how many decades of data an anomalous-transport claim actually requires, when a neural solver’s advantage belongs to the network and when it belongs to the comparison it was tested against. Stating those limits is what separates a mechanistic model from a fit that happens to look mechanistic.

His group releases its methods openly at github.com/Feugmo-Group, and works with industrial partners in the electric-vehicle battery supply chain alongside its academic collaborations.

• • Methods. Differentiable physics · Physics-informed neural networks · Spectral and spectral-element solvers · Automatic differentiation · Machine-learned interatomic potentials · Classical and dynamical density functional theory · Neural functional theory · Differentiable optimization and inverse design · Sparse regression and symbolic discovery
  • Physics. Ion transport · Electric double layer · Charge transfer kinetics · Corrosion and passive-film growth · Degradation mechanisms · Defect chemistry · Phase behaviour · Anomalous diffusion and fractional transport · Identifiability and inverse problems
  • Systems. Solid-state and lithium-ion batteries · Solid oxide electrolysis cells · Fuel cells · Molten salts · High-entropy alloys · Nuclear materials · Nanoporous adsorbents and separations
  • Scales. Electronic structure → atomistic dynamics → interfacial statistical mechanics → device response

• Electrochemical modelling:

  Electrochemical systems span enormous ranges of length and time, yet they are often described using fitted models that lack mechanistic power. Our group develops differentiable physics-based programs for prediction, inverse modelling, uncertainty quantification, and design.

• Solvers for stiff physics:

  We develop neural and spectral methods for challenging Poisson–Nernst–Planck transport problems. Our Neural Spectral-Element Method uses fixed spectral nodes and precomputed differentiation matrices to achieve highly accurate solutions.

• Mechanistic models of interfaces:

  Our differentiable density-functional-theory tools predict adsorption and double-layer structure without fitted parameters. We also develop learnable free-energy functionals combining statistical mechanics and machine learning.

• Structure by optimization:

  TorchDisorder reconstructs disordered materials from diffraction data through gradient-based optimization rather than stochastic sampling. These structures support molecular simulations of batteries, molten salts, electrolytes, and advanced alloys.

• Identifiability and applications:

  We determine not only which parameters fit experimental data, but also which parameters the data can actually identify. Our methods apply to batteries, fuel cells, electrolysers, molten-salt reactors, high-entropy alloys, corrosion, carbon capture, and hydrogen storage.

• Open science and future goals:

  Our tools are available at

  github.com/Feugmo-Group.

  We aim to make electrochemical modelling an inverse science: recovering mechanisms with quantified confidence and using models to design better materials.

• 2018, PhD, Computational Chemistry, University of Namur, Belgium

• 2011, Mphil, 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

• 2023-present, Science Faculty Council, Chemistry Representative

• 2024-present, Chemistry Awards Committee

• 2022-2024, Equity and Diversity Council, Science Representative

• 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
• CHEM 400 - Special Topics in Chemistry
  • Taught in 2026
• CHEM 740 - Selected Topics in Theoretical Chemistry
  • Taught in 2026
• NE 451 - Simulation Methods
  • Taught in 2023, 2024, 2025, 2026
• NE 452 - Special Topics in Nanoscale Simulations
  • Taught in 2026

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