70 citations · 125 across the 6 of their papers we have counts for
3 papers · 1 filter
Hierarchical Learning in Euclidean Neural Networks
Joshua A. Rackers, Pranav Rao
Equivariant machine learning methods have shown wide success at 3D learning applications in recent years. These models explicitly build in the reflection, translation and rotation…
Accurate Hellmann-Feynman forces from density functional calculations with augmented Gaussian basis sets
Shivesh Pathak, Ignacio Ema López, Alex J. Lee +4
The Hellmann-Feynman (HF) theorem provides a way to compute forces directly from the electron density, enabling efficient force calculations for large systems through machine learn…
Cracking the Quantum Scaling Limit with Machine Learned Electron Densities
Joshua A. Rackers, Lucas Tecot, Mario Geiger +1
A long-standing goal of science is to accurately solve the Schrödinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical c…