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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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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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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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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Vibronic coupling in asymmetric bichromophores: Theory and application to diphenylmethane-d5 von Nebgen Benjamin, Slipchenko, Lyudmila V

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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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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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The Rise of Neural Networks for Materials and Chemical Dynamics von Kulichenko, Maksim, Smith, Justin S, Nebgen, Benjamin, Li, Ying Wai, Fedik, Nikita, Boldyrev, Alexander I, Lubbers, Nicholas, Barros, Kipton, Tretiak, Sergei

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Intermolecular conical intersections in molecular aggregates von De Sio, Antonietta, Sommer, Ephraim, Nguyen, Xuan Trung, Groß, Lynn, Popović, Duško, Nebgen, Benjamin Tyler, Fernandez-Alberti, Sebastian, Pittalis, Stefano, Rozzi, Carlo Andrea, Molinari, Elisa, Mena-Osteritz, Elena, Bäuerle, Peter, Frauenheim, Thomas, Tretiak, Sergei, Lienau, Christoph

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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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Synergy of semiempirical models and machine learning in computational chemistry von Fedik, Nikita, Nebgen, Benjamin, Lubbers, Nicholas, Barros, Kipton, Kulichenko, Maksim, Li, Ying Wai, Zubatyuk, Roman, Messerly, Richard, Isayev, Olexandr, Tretiak, Sergei

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Predicting phosphorescence energies and inferring wavefunction localization with machine learning von Sifain, Andrew E, Lystrom, Levi, Messerly, Richard A, Smith, Justin S, Nebgen, Benjamin, Barros, Kipton, Tretiak, Sergei, Lubbers, Nicholas, Gifford, Brendan J

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Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles von Habib, Adela, Lubbers, Nicholas, Tretiak, Sergei, Nebgen, Benjamin

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Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks von Zubatuk, Tetiana, Nebgen, Benjamin Tyler, Lubbers, Nicholas Edward, Smith, Justin Steven, Zubatuk, Roman, Zhou, Guoqing, Koh, Christopher Fu-Chai, Barros, Kipton Marcos, Isayev, Olexandr, Tretiak, Sergei

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The effects of site asymmetry on near-degenerate state-to-state vibronic mixing in flexible bichromophores von Kidwell, Nathanael M., Nebgen, Benjamin Tyler, Slipchenko, Lyudmila V., Zwier, Timothy S.

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Vibronic coupling in asymmetric bichromophores: theory and application to diphenylmethane-d(5) von Nebgen, Benjamin, Slipchenko, Lyudmila V

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Vibronic coupling in asymmetric bichromophores: Theory and application to diphenylmethane- d 5 von Nebgen, Benjamin, Slipchenko, Lyudmila V.

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Using Machine Learning Hamiltonians To Compute Molecular Motor Barrier Heights von Philip, Aaron, Zhou, Guoqing, Nebgen, Benjamin

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