2.1k citations · 3.9k across the 15 of their papers we have counts for
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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…
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…