Quantum-chemical insights from deep tensor neural networks von Schütt, Kristof T., Arbabzadah, Farhad, Chmiela, Stefan, Müller, Klaus R., Tkatchenko, Alexandre

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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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Membrane separation study for methane-hydrogen gas mixtures by molecular simulations von Kovács, Papp, Kristóf

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Perspective on integrating machine learning into computational chemistry and materials science von Westermayr, Julia, Gastegger, Michael, Schütt, Kristof T., Maurer, Reinhard J.

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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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Inverse design of 3d molecular structures with conditional generative neural networks von Gebauer, Niklas W. A., Gastegger, Michael, Hessmann, Stefaan S. P., Müller, Klaus-Robert, Schütt, Kristof T.

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Machine learning of accurate energy-conserving molecular force fields von Chmiela, Stefan, Tkatchenko, Alexandre, Sauceda, Huziel E, Poltavsky, Igor, Schütt, Kristof T, Müller, Klaus-Robert

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Higher-Order Explanations of Graph Neural Networks via Relevant Walks von Schnake, Thomas, Eberle, Oliver, Lederer, Jonas, Nakajima, Shinichi, Schutt, Kristof T., Muller, Klaus-Robert, Montavon, Gregoire

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Machine learning of solvent effects on molecular spectra and reactions von Gastegger, Michael, Schütt, Kristof T, Müller, Klaus-Robert

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The (Un)reliability of Saliency Methods von Kindermans, Pieter-Jan, Hooker, Sara, Adebayo, Julius, Alber, Maximilian, Schütt, Kristof T., Dähne, Sven, Erhan, Dumitru, Kim, Been

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Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning von Pronobis, Wiktor, Schütt, Kristof T., Tkatchenko, Alexandre, Müller, Klaus-Robert

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SchNetPack 2.0: A neural network toolbox for atomistic machine learning von Schütt, Kristof T., Hessmann, Stefaan S. P., Gebauer, Niklas W. A., Lederer, Jonas, Gastegger, Michael

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PILOT: equivariant diffusion for pocket-conditioned de novo ligand generation with multi-objective guidance via importance sampling von Cremer, Julian, Le, Tuan, Noé, Frank, Clevert, Djork-Arné, Schütt, Kristof T

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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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A data science roadmap for open science organizations engaged in early-stage drug discovery von Edfeldt, Kristina, Edwards, Aled M., Engkvist, Ola, Günther, Judith, Hartley, Matthew, Hulcoop, David G., Leach, Andrew R., Marsden, Brian D., Menge, Amelie, Misquitta, Leonie, Müller, Susanne, Owen, Dafydd R., Schütt, Kristof T., Skelton, Nicholas, Steffen, Andreas, Tropsha, Alexander, Vernet, Erik, Wang, Yanli, Wellnitz, James, Willson, Timothy M., Clevert, Djork-Arné, Haibe-Kains, Benjamin, Schiavone, Lovisa Holmberg, Schapira, Matthieu

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Machine Learning Meets Quantum Physics von Schütt, Kristof T, Chmiela, Stefan, von Lilienfeld, O. Anatole, Tkatchenko, Alexandre, Tsuda, Koji, Müller, Klaus-Robert

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Autonomous robotic nanofabrication with reinforcement learning von Leinen, Philipp, Esders, Malte, Schütt, Kristof T., Wagner, Christian, Müller, Klaus-Robert, Tautz, F. Stefan

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An analytic solution for the magnetization of two-dimensional ferrofluids based on the mean spherical approximation von Kristóf, T, Boda, D, Szalai, I

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Simulation study of a rectifying bipolar ion channel: Detailed model versus reduced model von Ható, Boda, Gillespie, Vrabec, Rutkai, Kristóf

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Corrosion testing and evaluation of gas oil desulfurization reactor structure material in the presence of fatty acids von Lukács, Z., Molnár, F., Kovács, I., Wáhl Horváth, I., Hancsók, J., Kristóf, T.

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