John E. Herr

发表

Fourier Series for Singular Measures

Eric S. Weber, John E. Herr, E. Weber, 2015, Axioms.

Identifying complex Hadamard submatrices of the Fourier matrices via primitive sets

John E. Herr, Troy M. Wiegand, 2021 .

Graph Neural Networks for Predicting Chemical Reaction Performance

Nitesh V. Chawla, Mandana Saebi, John E. Herr, 2021 .

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

David W Toth, John E. Herr, John A. Parkhill, 2017, Chemical science.

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences.

John E. Herr, John A. Parkhill, John A Parkhill, 2019, The Journal of chemical physics.

Bond Energies from a Diatomics-in-Molecules Neural Network

John E. Herr, John A. Parkhill, John A Parkhill, 2017, 1703.08640.

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network.

John E. Herr, John A. Parkhill, John A Parkhill, 2017, The journal of physical chemistry letters.

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Benjamin P. Pritchard, David L. Dotson, John E. Herr, 2022, Scientific Data.

End-to-end differentiable construction of molecular mechanics force fields

John E. Herr, J. Chodera, Ivy Zhang, 2022, Chemical science.

Few-Shot Graph Learning for Molecular Property Prediction

Nitesh V. Chawla, Wenhao Yu, Meng Jiang, 2021, WWW.

Positive matrices in the Hardy space with prescribed boundary representations via the Kaczmarz algorithm

John E. Herr, P. Jorgensen, E. Weber, 2016, Journal d'Analyse Mathématique.

FA ] 2 M ay 2 01 6 FOURIER SERIES FOR SINGULAR MEASURES

John E. Herr, E. Weber, 2018 .

A matrix characterization of boundary representations of positive matrices in the Hardy space

John E. Herr, P. Jorgensen, E. Weber, 2017, 1705.04198.

Fourier series for singular measures and the Kaczmarz algorithm

John E. Herr, 2016 .

Origin of the Size-Dependent Stokes Shift in CsPbBr3 Perovskite Nanocrystals.

John E. Herr, John A. Parkhill, S. Rouvimov, 2017, Journal of the American Chemical Society.

A characterization of boundary representations of positive matrices in the Hardy space via the Abel product

John E. Herr, P. Jorgensen, E. Weber, 2018, Linear Algebra and its Applications.

Metadynamics for training neural network model chemistries: A competitive assessment.

John E Herr, Kun Yao, Ryker McIntyre, 2017, The Journal of chemical physics.

The many-body expansion combined with neural networks.

John E Herr, Kun Yao, John Parkhill, 2016, The Journal of chemical physics.

On the use of real-world datasets for reaction yield prediction

John E. Herr, N. Chawla, O. Wiest, 2021, Chemical Science.

Origin of the Size-Dependent Stokes Shift in CsPbBr3 Perovskite Nanocrystals.

John E. Herr, John A. Parkhill, S. Rouvimov, 2017, Journal of the American Chemical Society.

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences.

John E. Herr, John A. Parkhill, Kun Yao, 2018, The Journal of chemical physics.