Optimal radial basis for density-based atomic representations. - Alexander Goscinski; Felix Musil; Sergey Pozdnyakov; Michele Ceriotti (CoRR, 2021)

Incompleteness of graph convolutional neural networks for points clouds in three dimensions. - Sergey N. Pozdnyakov; Michele Ceriotti (CoRR, 2022)

Incompleteness of graph neural networks for points clouds in three dimensions. - Sergey N. Pozdnyakov; Michele Ceriotti (Mach. Learn. Sci. Technol., 2022)

Smooth, exact rotational symmetrization for deep learning on point clouds. - Sergey Pozdnyakov; Michele Ceriotti (NeurIPS, 2023)

Completeness of Atomic Structure Representations. - Jigyasa Nigam; Sergey N. Pozdnyakov; Kevin K. Huguenin-Dumittan; Michele Ceriotti (CoRR, 2023)

Wigner kernels: body-ordered equivariant machine learning without a basis. - Filippo Bigi; Sergey N. Pozdnyakov; Michele Ceriotti (CoRR, 2023)

Probing the effects of broken symmetries in machine learning. - Marcel F. Langer; Sergey N. Pozdnyakov; Michele Ceriotti (Mach. Learn. Sci. Technol., 2024)

PET-MAD, a universal interatomic potential for advanced materials modeling. - Arslan Mazitov; Filippo Bigi; Matthias Kellner; Paolo Pegolo; Davide Tisi; Guillaume Fraux; Sergey Pozdnyakov; Philip Loche; Michele Ceriotti (CoRR, 2025)