Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space von Hansen, Katja, Biegler, Franziska, Ramakrishnan, Raghunathan, Pronobis, Wiktor, von Lilienfeld, O. Anatole, Müller, Klaus-Robert, Tkatchenko, Alexandre

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Machine Learning for Molecular Simulation von Noé, Frank, Tkatchenko, Alexandre, Müller, Klaus-Robert, Clementi, Cecilia

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Towards Explainable Artificial Intelligence von Samek, Wojciech, Müller, Klaus-Robert

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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems von Keith, John A, Vassilev-Galindo, Valentin, Cheng, Bingqing, Chmiela, Stefan, Gastegger, Michael, Müller, Klaus-Robert, Tkatchenko, Alexandre

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Unmasking Clever Hans predictors and assessing what machines really learn von Lapuschkin, Sebastian, Wäldchen, Stephan, Binder, Alexander, Montavon, Grégoire, Samek, Wojciech, Müller, Klaus-Robert

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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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Methods for interpreting and understanding deep neural networks von Montavon, Grégoire, Samek, Wojciech, Müller, Klaus-Robert

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Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules von Pronobis, Wiktor, Tkatchenko, Alexandre, Müller, Klaus-Robert

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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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Towards exact molecular dynamics simulations with machine-learned force fields von Chmiela, Stefan, Sauceda, Huziel E., Müller, Klaus-Robert, Tkatchenko, Alexandre

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Explaining nonlinear classification decisions with deep Taylor decomposition von Montavon, Grégoire, Lapuschkin, Sebastian, Binder, Alexander, Samek, Wojciech, Müller, Klaus-Robert

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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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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation von Bach, Sebastian, Binder, Alexander, Montavon, Grégoire, Klauschen, Frederick, Müller, Klaus-Robert, Samek, Wojciech

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Bypassing the Kohn-Sham equations with machine learning von Brockherde, Felix, Vogt, Leslie, Li, Li, Tuckerman, Mark E., Burke, Kieron, Müller, Klaus-Robert

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Quantum chemical accuracy from density functional approximations via machine learning von Bogojeski, Mihail, Vogt-Maranto, Leslie, Tuckerman, Mark E., Müller, Klaus-Robert, Burke, Kieron

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Interpretable deep neural networks for single-trial EEG classification von Sturm, Irene, Lapuschkin, Sebastian, Samek, Wojciech, Müller, Klaus-Robert

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sGDML: Constructing accurate and data efficient molecular force fields using machine learning von Chmiela, Stefan, Sauceda, Huziel E., Poltavsky, Igor, Müller, Klaus-Robert, Tkatchenko, Alexandre

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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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Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies von Hansen, Katja, Montavon, Grégoire, Biegler, Franziska, Fazli, Siamac, Rupp, Matthias, Scheffler, Matthias, von Lilienfeld, O. Anatole, Tkatchenko, Alexandre, Müller, Klaus-Robert

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Layer-Wise Relevance Propagation: An Overview von Montavon, Grégoire, Binder, Alexander, Lapuschkin, Sebastian, Samek, Wojciech, Müller, Klaus-Robert

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