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Found 5 papers, 1 papers with code
Probing the effects of broken symmetries in machine learning
We focus specifically on physical observables that are likely to be affected -- directly or indirectly -- by symmetry breaking, finding negligible consequences when the model is used in an interpolative, bulk, regime.
Smooth, exact rotational symmetrization for deep learning on point clouds
Point clouds are versatile representations of 3D objects and have found widespread application in science and engineering.
Wigner kernels: body-ordered equivariant machine learning without a basis
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
Completeness of Atomic Structure Representations
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.
Incompleteness of graph neural networks for points clouds in three dimensions
We construct pairs of distinct point clouds whose associated graphs are, for any cutoff radius, equivalent based on a first-order Weisfeiler-Lehman test.
