no code implementations • NeurIPS 2023 • Sergey N. Pozdnyakov, Michele Ceriotti
Point clouds are versatile representations of 3D objects and have found widespread application in science and engineering.
no code implementations • 7 Mar 2023 • Filippo Bigi, Sergey N. Pozdnyakov, Michele Ceriotti
Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials.
Ranked #2 on Formation Energy on QM9
no code implementations • 28 Feb 2023 • Jigyasa Nigam, Sergey N. Pozdnyakov, Kevin K. Huguenin-Dumittan, Michele Ceriotti
In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry.
no code implementations • 18 Jan 2022 • Sergey N. Pozdnyakov, Michele Ceriotti
We construct pairs of distinct point clouds whose associated graphs are, for any cutoff radius, equivalent based on a first-order Weisfeiler-Lehman test.