872 citations · 2.9k across the 35 of their papers we have counts for
4 papers · 1 filter
Machine learning for molecular dynamics with strongly correlated electrons
Hidemaro Suwa, Justin S. Smith, Nicholas Lubbers +3
We use machine learning to enable large-scale molecular dynamics (MD) of a correlated electron model under the Gutzwiller approximation scheme. This model exhibits a Mott transitio…
Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness
Nicholas Lubbers, David C. Bolton, Jamaludin Mohd-Yusof +3
Machine learning regression can predict macroscopic fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions.…
Transferable Molecular Charge Assignment Using Deep Neural Networks
Ben Nebgen, Nick Lubbers, Justin S. Smith +6
We use HIP-NN, a neural network architecture that excels at predicting molecular energies, to predict atomic charges. The charge predictions are accurate over a wide range of molec…
Less is more: sampling chemical space with active learning
Justin S. Smith, Ben Nebgen, Nicholas Lubbers +2
The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train suc…