| The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics K Yao, JE Herr, DW Toth, R Mckintyre, J Parkhill Chemical science 9 (8), 2261-2269, 2018 | 449 | 2018 |
| Origin of the Size-Dependent Stokes Shift in CsPbBr3 Perovskite Nanocrystals MC Brennan, JE Herr, TS Nguyen-Beck, J Zinna, S Draguta, S Rouvimov, ... Journal of the American Chemical Society 139 (35), 12201-12208, 2017 | 293 | 2017 |
| Few-shot graph learning for molecular property prediction Z Guo, C Zhang, W Yu, J Herr, O Wiest, M Jiang, NV Chawla Proceedings of the web conference 2021, 2559-2567, 2021 | 141 | 2021 |
| Intrinsic bond energies from a bonds-in-molecules neural network K Yao, JE Herr, SN Brown, J Parkhill The journal of physical chemistry letters 8 (12), 2689-2694, 2017 | 124 | 2017 |
| The many-body expansion combined with neural networks K Yao, JE Herr, J Parkhill The Journal of chemical physics 146 (1), 2017 | 111 | 2017 |
| Metadynamics for training neural network model chemistries: A competitive assessment JE Herr, K Yao, R McIntyre, DW Toth, J Parkhill The Journal of chemical physics 148 (24), 2018 | 73 | 2018 |
| Spice, a dataset of drug-like molecules and peptides for training machine learning potentials P Eastman, PK Behara, DL Dotson, R Galvelis, JE Herr, JT Horton, Y Mao, ... Scientific Data 10 (1), 11, 2023 | 50 | 2023 |
| On the use of real-world datasets for reaction yield prediction M Saebi, B Nan, JE Herr, J Wahlers, Z Guo, AM Zurański, T Kogej, ... Chemical science 14 (19), 4997-5005, 2023 | 40 | 2023 |
| Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences JE Herr, K Koh, K Yao, J Parkhill The Journal of chemical physics 151 (8), 2019 | 31 | 2019 |
| End-to-end differentiable construction of molecular mechanics force fields Y Wang, J Fass, B Kaminow, JE Herr, D Rufa, I Zhang, I Pulido, M Henry, ... Chemical Science 13 (41), 12016-12033, 2022 | 29 | 2022 |
| End-to-end differentiable molecular mechanics force field construction Y Wang, J Fass, B Kaminow, JE Herr, D Rufa, I Zhang, I Pulido, M Henry, ... arXiv preprint arXiv:2010.01196, 2020 | 14 | 2020 |
| Graph neural networks for predicting chemical reaction performance M Saebi, B Nan, J Herr, J Wahlers, O Wiest, N Chawla | 8 | 2021 |
| Fourier Series for Fractals in Two Dimensions JE Herr, PET Jorgensen, ES Weber From Classical Analysis to Analysis on Fractals: A Tribute to Robert …, 2023 | 2 | 2023 |
| Bond Energies from a Diatomics-in-Molecules Neural Network K Yao, J Herr, S Brown, J Parkhill arXiv preprint arXiv:1703.08640, 2017 | 2 | 2017 |
| Compressing physical properties of atomic species for improving predictive chemistry JE Herr, K Koh, K Yao, J Parkhill arXiv preprint arXiv:1811.00123, 2018 | 1 | 2018 |
| Fourier series for singular measures in higher dimensions C Berner, JE Herr, PET Jorgensen, ES Weber arXiv preprint arXiv:2402.15950, 2024 | | 2024 |
| Supplementary information for: Development of neural network models for prediction of molecular properties JE Herr | | 2020 |
| Rationalizing the size-dependent Stokes shift in CsPbBr3 nanocrystals M Brennan, J Herr, T Nguyen-Beck, J Parkhill, M Kuno ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 255, 2018 | | 2018 |
John ParkhillThe University of Notre Dame在 nd.edu 的电子邮件经过验证
