1.9k citations · 2k across the 6 of their papers we have counts for
6 papers
Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learning
Anders Johansson, Yu Xie, Cameron J. Owen +4
Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale of billions of atoms has been achieved for the heterogeneous catalytic system of H/Pt(111) using the…
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.…
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner, Albert Musaelian, Lixin Sun +6
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations f…
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.…
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
Yu Xie, Jonathan Vandermause, Lixin Sun +2
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions…
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…