39 citations · 39 across the 3 of their papers we have counts for
3 papers
Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation
Maksim Kulichenko, Kipton Barros, Nicholas Lubbers +5
Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based softwar…
Using Machine Learning Hamiltonians To Compute Molecular Motor Barrier Heights
Aaron Philip, Guoqing Zhou, Benjamin Nebgen
Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in use by computational chemists due to their combination of accuracy and speed. Yet, it is still not cle…
Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks
Tetiana Zubatyuk, Ben Nebgen, Nicholas Lubbers +7
The Hückel Hamiltonian is an incredibly simple tight-binding model famed for its ability to capture qualitative physics phenomena arising from electron interactions in molecules an…