Quantum-chemical insights from interpretable atomistic neural networks. - Kristof T. Schütt; Michael Gastegger; Alexandre Tkatchenko; Klaus-Robert Müller (CoRR, 2018)

Generating equilibrium molecules with deep neural networks. - Niklas W. A. Gebauer; Michael Gastegger; Kristof T. Schütt (CoRR, 2018)

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. - Niklas W. A. Gebauer; Michael Gastegger; Kristof Schütt (NeurIPS, 2019)

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks. - Kristof T. Schütt; Michael Gastegger; Alexandre Tkatchenko; Klaus-Robert Müller (Explainable AI, 2019)

Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics. - Julia Westermayr; Michael Gastegger; Philipp Marquetand (CoRR, 2020)

Equivariant message passing for the prediction of tensorial properties and molecular spectra. - Kristof Schütt; Oliver T. Unke; Michael Gastegger (ICML, 2021)

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities. - Oliver T. Unke; Mihail Bogojeski; Michael Gastegger; Mario Geiger; Tess E. Smidt; Klaus-Robert Müller (NeurIPS, 2021)

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects. - Oliver T. Unke; Stefan Chmiela; Michael Gastegger; Kristof T. Schütt; Huziel E. Sauceda; Klaus-Robert Müller (CoRR, 2021)

Inverse design of 3d molecular structures with conditional generative neural networks. - Niklas W. A. Gebauer; Michael Gastegger; Stefaan Simon Pierre Hessmann; Klaus-Robert Müller; Kristof T. Schütt (CoRR, 2021)

Automatic Identification of Chemical Moieties. - Jonas Lederer; Michael Gastegger; Kristof T. Schütt; Michael Kampffmeyer; Klaus-Robert Müller; Oliver T. Unke (CoRR, 2022)

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations. - Oliver T. Unke; Martin Stöhr; Stefan Ganscha; Thomas Unterthiner; Hartmut Maennel; Sergii Kashubin; Daniel Ahlin; Michael Gastegger; Leonardo Medrano Sandonas; Alexandre Tkatchenko; Klaus-Robert Müller (CoRR, 2022)

Relevant Walk Search for Explaining Graph Neural Networks. - Ping Xiong 002; Thomas Schnake; Michael Gastegger; Grégoire Montavon; Klaus-Robert Müller; Shinichi Nakajima (ICML, 2023)

Scaling machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points. - Malte Esders; Gimmy Alex Fernandez Ramirez; Michael Gastegger; Satya Swarup Samal (CoRR, 2023)

Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning. - Daniel Platero-Rochart; Tatyana Krivobokova; Michael Gastegger; Gilbert Reibnegger; Pedro A. Sánchez-Murcia (J. Chem. Inf. Model., 2023)

Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points. - Malte Esders; Gimmy Alex Fernandez Ramirez; Michael Gastegger; Satya Swarup Samal (Comput. Chem. Eng., 2024)

Improved motif-scaffolding with SE(3) flow matching. - Jason Yim; Andrew Campbell; Emile Mathieu; Andrew Y. K. Foong; Michael Gastegger; José Jiménez-Luna; Sarah Lewis; Victor Garcia Satorras; Bastiaan S. Veeling; Frank Noé; Regina Barzilay; Tommi S. Jaakkola (Trans. Mach. Learn. Res., 2024)