PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges von Unke, Oliver T, Meuwly, Markus

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A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information von Unke, Oliver T., Meuwly, Markus

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Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces von Unke, Oliver T, Meuwly, Markus

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SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects von Unke, Oliver T., Chmiela, Stefan, Gastegger, Michael, Schütt, Kristof T., Sauceda, Huziel E., Müller, Klaus-Robert

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Reactive dynamics and spectroscopy of hydrogen transfer from neural network-based reactive potential energy surfaces von Käser, Silvan, Unke, Oliver T, Meuwly, Markus

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Machine Learning Force Fields von Unke, Oliver T, Chmiela, Stefan, Sauceda, Huziel E, Gastegger, Michael, Poltavsky, Igor, Schütt, Kristof T, Tkatchenko, Alexandre, Müller, Klaus-Robert

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Isomerization and decomposition reactions of acetaldehyde relevant to atmospheric processes from dynamics simulations on neural network-based potential energy surfaces von Käser, Silvan, Unke, Oliver T., Meuwly, Markus

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A Euclidean transformer for fast and stable machine learned force fields von Frank, J. Thorben, Unke, Oliver T., Müller, Klaus-Robert, Chmiela, Stefan

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Exhaustive state-to-state cross sections for reactive molecular collisions from importance sampling simulation and a neural network representation von Koner, Debasish, Unke, Oliver T., Boe, Kyle, Bemish, Raymond J., Meuwly, Markus

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Molecular Oxygen Formation in Interstellar Ices Does Not Require Tunneling von Pezzella, Marco, Unke, Oliver T, Meuwly, Markus

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Complete and Efficient Covariants for Three-Dimensional Point Configurations with Application to Learning Molecular Quantum Properties von Maennel, Hartmut, Unke, Oliver T., Müller, Klaus-Robert

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Minimal distributed charges: Multipolar quality at the cost of point charge electrostatics von Unke, Oliver T., Devereux, Mike, Meuwly, Markus

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Accurate global machine learning force fields for molecules with hundreds of atoms von Chmiela, Stefan, Vassilev-Galindo, Valentin, Unke, Oliver T, Kabylda, Adil, Sauceda, Huziel E, Tkatchenko, Alexandre, Müller, Klaus-Robert

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Reactive atomistic simulations of Diels-Alder reactions: The importance of molecular rotations von Rivero, Uxía, Unke, Oliver T., Meuwly, Markus, Willitsch, Stefan

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Migration of small ligands in globins: Xe diffusion in truncated hemoglobin N von Diamantis, Polydefkis, Unke, Oliver T, Meuwly, Markus

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Sampling reactive regions in phase space by following the minimum dynamic path von Unke, Oliver T., Brickel, Sebastian, Meuwly, Markus

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High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning von Unke, Oliver T, Koner, Debasish, Patra, Sarbani, Käser, Silvan, Meuwly, Markus

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Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies von Vazquez-Salazar, Luis Itza, Boittier, Eric D, Unke, Oliver T, Meuwly, Markus

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Automatic identification of chemical moieties von Lederer, Jonas, Gastegger, Michael, Schütt, Kristof T, Kampffmeyer, Michael, Müller, Klaus-Robert, Unke, Oliver T

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HSO3Cl: a prototype molecule for studying OH-stretching overtone induced photodissociation von Yosa Reyes, Juvenal, Brickel, Sebastian, Unke, Oliver T, Nagy, Tibor, Meuwly, Markus

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