109 citations · 201 across the 6 of their papers we have counts for
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physics.chem-ph2017★ 81 cited
Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment
John E. Herr, Kun Yao, Ryker McIntyre +2
Neural network (NN) model chemistries (MCs) promise to facilitate the accurate exploration of chemical space and simulation of large reactive systems. One important path to improvi…
physics.chem-ph2017★ 2 cited
The TensorMol-0.1 Model Chemistry: a Neural Network Augmented with Long-Range Physics
Kun Yao, John E. Herr, David W. Toth +2
Traditional force-fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering…
physics.chem-ph2017
Bond Energies from a Diatomics-in-Molecules Neural Network
Kun Yao, John Herr, Seth Brown +1
Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy close to the ab-initio methods used to build them. B…