109 citations · 201 across the 6 of their papers we have counts for
6 papers
Compressing physical properties of atomic species for improving predictive chemistry
John E. Herr, Kevin Koh, Kun Yao +1
The answers to many unsolved problems lie in the intractable chemical space of molecules and materials. Machine learning techniques are rapidly growing in popularity as a way to co…
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…
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…
The Many-Body Expansion Combined with Neural Networks
Kun Yao, John E. Herr, John Parkhill
Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contr…
The Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks
Kun Yao, John Parkhill
We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from electron density. The output of the network is used as a non-lo…