872 citations · 1.1k across the 13 of their papers we have counts for
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physics.chem-ph2018★ 2 cited
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…
physics.comp-ph2018★ 872 cited
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…