Search Results for author:
Found 4 papers, 1 papers with code
A prediction rigidity formalism for low-cost uncertainties in trained neural networks
Regression methods are fundamental for scientific and technological applications.
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
Fast evaluation of spherical harmonics with sphericart
Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in physical and theoretical chemistry as well as in different fields of science and technology, from geology and atmospheric sciences to signal processing and computer graphics.
A smooth basis for atomistic machine learning
Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighbourhood of each atom in the system.
