109 citations · 334 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
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…
Orbital optimization in the perfect pairing hierarchy. Applications to full-valence calculations on linear polyacenes
Susi Lehtola, John Parkhill, Martin Head-Gordon
We describe the implementation of orbital optimization for the models in the perfect pairing hierarchy [Lehtola et al, J. Chem. Phys. 145, 134110 (2016)]. Orbital optimization, whi…
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…