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The Journal of Chemical Physics|January 9, 2017
The many-body expansion combined with neural networksKun Yao, John E Herr, John ParkhillThe Journal of Chemical Physics|September 1, 2019
Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciencesJohn E Herr, Kevin Koh, Kun Yao, et al.The Journal of Physical Chemistry Letters|June 3, 2017
Intrinsic Bond Energies from a Bonds-in-Molecules Neural NetworkKun Yao, John E Herr, Seth N Brown, et al.The Journal of Chemical Physics|July 2, 2018
Metadynamics for training neural network model chemistries: A competitive assessmentJohn E Herr, Kun Yao, Ryker McIntyre, et al.Chemical Science|May 3, 2018
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physicsKun Yao, John E Herr, David W Toth, et al.Journal of the American Chemical Society|August 4, 2017
Origin of the Size-Dependent Stokes Shift in CsPbBr<sub>3</sub> Perovskite NanocrystalsMichael C Brennan, John E Herr, Triet S Nguyen-Beck, et al.Chemical Science|November 9, 2022
End-to-end differentiable construction of molecular mechanics force fieldsYuanqing Wang, Josh Fass, Benjamin Kaminow, et al.Chemical Science|May 19, 2023
On the use of real-world datasets for reaction yield predictionMandana Saebi, Bozhao Nan, John E Herr, et al.Scientific Data|January 4, 2023
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning PotentialsPeter Eastman, Pavan Kumar Behara, David L Dotson, et al.Pageof 1