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publications
The many-body expansion combined with neural networks
Published in The Journal of Chemical Physics, 2017
Combining neural networks with a many-body expansion for potential models of condensed phase systems.
Recommended citation: Yao, K.; Herr, J. E.; Parkhill, J. The Journal of Chemical Physics 2016, 146, 014106.
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Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
Published in The Journal of Physical Chemistry Letters, 2017
A high-dimensional neural network model which predicts bond energies in molecules.
Recommended citation: Yao, K.; Herr, J. E.; Brown, J. S. N.; Parkhill, The Journal of Physical Chemistry Letters 2017, 8, 2689.
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Origin of the Size-Dependent Stokes Shift in CsPbBr3 Perovskite Nanocrystals
Published in The Journal of the American Chemical Society, 2017
Recommended citation: Brennan, M. C.; Herr, J. E.; Nguyen-Beck, T. S.; Zinna, J.; Draguta, S.; Rou- vimov, S.; Parkhill, J.; Kuno, M. Journal of the American Chemical Society 2017, 139, 12201–12208.
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The TensorMol-0.1 model chemistry: A neural network augmented with long-range physics
Published in Chemical Science, 2018
High-dimensional neural network potential for energies, forces, atomic charges, and dipoles.
Recommended citation: Yao, K.; Herr, J. E.; Toth, D. W.; Mckintyre, R.; Parkhill, J. Chemical science 2018, 9, 2261–2269.
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Metadynamics for training neural network model chemistries: A competitive assessment
Published in The Journal of Chemical Physics, 2018
Metadynamics for generating non-equilibrium molecular geometry datasets.
Recommended citation: Herr, J. E.; Yao, K.; McIntyre, R.; Toth, D. W.; Parkhill, J. The Journal of chemical physics 2018, 148, 241710.
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Compressing physics with an autoencoder Creating an atomic species representation to improve machine learning models in the chemical sciences
Published in The Journal of Chemical Physics, 2019
An updated TensorMol model which which is fully transferable and training with many unique elements.
Recommended citation: Herr, J. E.; Koh, K.; Yao, K.; Parkhill, J. The Journal of Chemical Physics 2019, 151, 084103.
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Few-shot graph learning for molecular property prediction
Published in Proceedings of the Web Conference 2021, 2021
Using few-shot learning techniques for predicting molecular properties based on graph neural networks.
Recommended citation: Guo, Z., Zhang, C., Yu, W., Herr, J., Wiest, O., Jiang, M., & Chawla, N. V. Proceedings of the Web Conference 2021, 2559–2567.
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End-to-end differentiable construction of molecular mechanics force fields
Published in Chemical Science, 2022
A method for constructing molecular mechanics force fields using differentiable programming techniques.
Recommended citation: Wang, Y., Fass, J., Kaminow, B., Herr, J. E., et al. Chemical Science 2022, 13, 12016–12033.
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Spice: A dataset of drug-like molecules and peptides for ML potentials
Published in Scientific Data, 2023
A dataset of drug-like molecules and peptides designed for training machine learning potentials.
Recommended citation: Eastman, P., Behara, P. K., Dotson, D. L., Galvelis, R., Herr, J. E., et al. Scientific Data 2023, 10, 11.
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On the use of real-world datasets for reaction yield prediction
Published in Chemical Science, 2023
Investigating the use of real-world datasets for predicting reaction yields in chemical synthesis.
Recommended citation: Saebi, M., Nan, B., Herr, J. E., Wahlers, J., Guo, Z., et al. Chemical Science 2023, 14, 4997–5005.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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