Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning von Smith, Justin S., Nebgen, Benjamin T., Zubatyuk, Roman, Lubbers, Nicholas, Devereux, Christian, Barros, Kipton, Tretiak, Sergei, Isayev, Olexandr, Roitberg, Adrian E.

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Teaching a neural network to attach and detach electrons from molecules von Zubatyuk, Roman, Smith, Justin S., Nebgen, Benjamin T., Tretiak, Sergei, Isayev, Olexandr

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Discovering a Transferable Charge Assignment Model Using Machine Learning von Sifain, Andrew E, Lubbers, Nicholas, Nebgen, Benjamin T, Smith, Justin S, Lokhov, Andrey Y, Isayev, Olexandr, Roitberg, Adrian E, Barros, Kipton, Tretiak, Sergei

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Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential von Zhang, Shuhao, Makoś, Małgorzata Z., Jadrich, Ryan B., Kraka, Elfi, Barros, Kipton, Nebgen, Benjamin T., Tretiak, Sergei, Isayev, Olexandr, Lubbers, Nicholas, Messerly, Richard A., Smith, Justin S.

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Photoexcited Nonadiabatic Dynamics of Solvated Push–Pull π‑Conjugated Oligomers with the NEXMD Software von Sifain, Andrew E., Bjorgaard, Josiah A., Nelson, Tammie R., Nebgen, Benjamin T., White, Alexander J., Gifford, Brendan J., Gao, David W., Prezhdo, Oleg V., Fernandez-Alberti, Sebastian, Roitberg, Adrian E., Tretiak, Sergei

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Dynamics of a Myoglobin Mutant Enzyme: 2D IR Vibrational Echo Experiments and Simulations von Bagchi, Sayan, Nebgen, Benjamin T, Loring, Roger F, Fayer, M. D

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A neural network for determination of latent dimensionality in non-negative matrix factorization von Nebgen, Benjamin T, Vangara, Raviteja, Hombrados-Herrera, Miguel A, Kuksova, Svetlana, Alexandrov, Boian S

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Design principles from multiscale simulations to predict nanostructure in self-assembling ionic liquids von Nebgen, Benjamin T, Magurudeniya, Harsha D, Kwock, Kevin W C, Ringstrand, Bryan S, Ahmed, Towfiq, Seifert, Sönke, Zhu, Jian-Xin, Tretiak, Sergei, Firestone, Millicent A

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Design principles from multiscale simulations to predict nanostructure in self-assembling ionic liquidsElectronic supplementary information (ESI) available: Comparison of force fie... von Nebgen, Benjamin T, Magurudeniya, Harsha D, Kwock, Kevin W. C, Ringstrand, Bryan S, Ahmed, Towfiq, Seifert, Sönke, Zhu, Jian-Xin, Tretiak, Sergei, Firestone, Millicent A

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Machine learning potentials with Iterative Boltzmann Inversion: training to experiment von Matin, Sakib, Allen, Alice, Smith, Justin S, Lubbers, Nicholas, Jadrich, Ryan B, Messerly, Richard A, Nebgen, Benjamin T, Li, Ying Wai, Tretiak, Sergei, Barros, Kipton

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A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization von Nebgen, Benjamin T, Vangara, Raviteja, Hombrados-Herrera, Miguel A, Kuksova, Svetlana, Alexandrov, Boian S

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Machine learning potentials with Iterative Boltzmann Inversion: training to experiment von Matin, Sakib, Allen, Alice, Smith, Justin S, Lubbers, Nicholas, Jadrich, Ryan B, Messerly, Richard A, Nebgen, Benjamin T, Li, Ying Wai, Tretiak, Sergei, Barros, Kipton

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A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization von Nebgen, Benjamin T, Vangara, Raviteja, Hombrados-Herrera, Miguel A, Kuksova, Svetlana, Alexandrov, Boian S

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Automated discovery of a robust interatomic potential for aluminum von Smith, Justin S., Nebgen, Benjamin, Mathew, Nithin, Chen, Jie, Lubbers, Nicholas, Burakovsky, Leonid, Tretiak, Sergei, Nam, Hai Ah, Germann, Timothy, Fensin, Saryu, Barros, Kipton

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Less is more: Sampling chemical space with active learning von Smith, Justin S., Nebgen, Ben, Lubbers, Nicholas, Isayev, Olexandr, Roitberg, Adrian E.

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The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules von Smith, Justin S., Zubatyuk, Roman, Nebgen, Benjamin, Lubbers, Nicholas, Barros, Kipton, Roitberg, Adrian E., Isayev, Olexandr, Tretiak, Sergei

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Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials von Nelson, Tammie R, White, Alexander J, Bjorgaard, Josiah A, Sifain, Andrew E, Zhang, Yu, Nebgen, Benjamin, Fernandez-Alberti, Sebastian, Mozyrsky, Dmitry, Roitberg, Adrian E, Tretiak, Sergei

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Lightweight and effective tensor sensitivity for atomistic neural networks von Chigaev, Michael, Smith, Justin S., Anaya, Steven, Nebgen, Benjamin, Bettencourt, Matthew, Barros, Kipton, Lubbers, Nicholas

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Bond order predictions using deep neural networks von Magedov, Sergey, Koh, Christopher, Malone, Walter, Lubbers, Nicholas, Nebgen, Benjamin

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Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics von Zhou, Guoqing, Lubbers, Nicholas, Barros, Kipton, Tretiak, Sergei, Nebgen, Benjamin

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