5 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.LG2022
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…
physics.chem-ph2022★ 5 cited
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…
physics.chem-ph2021
A Polarizable Water Potential Derived from a Model Electron Density
Joshua A. Rackers, Roseane R. Silva, Zhi Wang +1
A new empirical potential for efficient, large scale molecular dynamics simulation of water is presented. The HIPPO (Hydrogen-like Intermolecular Polarizable POtential) force field…