Publications

Accurate and scalable exchange-correlation with deep learning
Giulia Luise, Chin-Wei Huang, Thijs Vogels, Derk P. Kooi, Sebastian Ehlert, Stephanie Lanius, Klaas J. H. Giesbertz, Amir Karton, Deniz Gunceler, Megan Stanley, Wessel P. Bruinsma, Lin Huang, Xinran Wei, José Garrido Torres, Abylay Katbashev, Rodrigo Chavez Zavaleta, Bálint Máté, Sékou-Oumar Kaba, Roberto Sordillo, Yingrong Chen, David B. Williams-Young, Christopher M. Bishop, Jan Hermann, Rianne van den Berg, Paola Gori-Giorgi
Preprint
arXiv:2506.14665

Improving equivariant networks with probabilistic symmetry breaking
Hannah Lawrence*, Vasco Portilheiro*, Yan Zhang, Sékou-Oumar Kaba
International Conference on Learning Representations (ICLR), 2025 arXiv:2503.21985

SymmCD: Symmetry-preserving crystal generation with diffusion models
Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Qiang Zhu, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh
International Conference on Learning Representations (ICLR), 2025 arXiv:2502.03638

(Oral) Symmetry breaking and equivariant neural networks
Sékou-Oumar Kaba, Siamak Ravanbakhsh
NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations, 2023
arXiv:2312.09016

Equivariant adaptation of large pre-trained models
Arnab Kumar Mondal*, Siba Smarak Panigrahi*, Sékou-Oumar Kaba, Sai Rajeswar, Siamak Ravanbakhsh
Advances in Neural Information Processing Systems (NeurIPS), 2023
arXiv:2310.01647

Equivariance with learned canonicalization functions
Sékou-Oumar Kaba*, Arnab Kumar Mondal*, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh
International Conference on Machine Learning (ICML), 2023
arXiv:2211.06489

Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
Daniel Levy*, Sékou-Oumar Kaba*, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh
ICML 2023 Workshop on Machine Learning for Astrophysics, 2023
arXiv:2211.06489

Prediction of large magnetic moment materials with graph neural networks and random forests
Sékou-Oumar Kaba, Benjamin Groleau-Paré, Marc-Antoine Gauthier, A-MS Tremblay, Simon Verret, Chloé Gauvin-Ndiaye
Physical Review Materials
arXiv:2111.14712

Equivariant networks for crystal structures
Sékou-Oumar Kaba, Siamak Ravanbakhsh
Advances in Neural Information Processing Systems (NeurIPS), 2022
arXiv:2211.15420

(Oral) Equivariance with learned canonicalization functions
Sékou-Oumar Kaba*, Arnab Kumar Mondal*, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh
NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022
arXiv:2211.06489

Gradient starvation: A learning proclivity in neural networks
Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron Courville, Doina Precup, Guillaume Lajoie
Advances in Neural Information Processing Systems (NeurIPS), 2021
arXiv:2011.09468

Group-theoretical classification of superconducting states of strontium ruthenate
Sékou-Oumar Kaba, David Sénéchal
Physical Review B, 100 (21), 214507, 2019
arXiv:1905.10467