22 citations · 22 across the 2 of their papers we have counts for
4 papers · 1 filter
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
Albert Musaelian, Simon Batzner, Anders Johansson +4
A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences.…
Multitask machine learning of collective variables for enhanced sampling of rare events
Lixin Sun, Jonathan Vandermause, Simon Batzner +4
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics.…
Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems
Jonathan P. Mailoa, Mordechai Kornbluth, Simon L. Batzner +5
Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents th…
On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events
Jonathan Vandermause, Steven B. Torrisi, Simon Batzner +4
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training effic…