Migration of small ligands in globins: Xe diffusion in truncated hemoglobin N. - Polydefkis Diamantis; Oliver T. Unke; Markus Meuwly (PLoS Comput. Biol., 2017)

Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces. - Oliver T. Unke; Markus Meuwly (J. Chem. Inf. Model., 2017)

High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning. - Oliver T. Unke; Debasish Koner; Sarbani Patra; Silvan Käser; Markus Meuwly (Mach. Learn. Sci. Technol., 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)

So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems. - J. Thorben Frank; Oliver T. Unke; Klaus-Robert Müller (NeurIPS, 2022)

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)

So3krates - Self-attention for higher-order geometric interactions on arbitrary length-scales. - J. Thorben Frank; Oliver T. Unke; Klaus-Robert Müller (CoRR, 2022)

From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields. - J. Thorben Frank; Oliver T. Unke; Klaus-Robert Müller; Stefan Chmiela (CoRR, 2023)

E3x: E(3)-Equivariant Deep Learning Made Easy. - Oliver T. Unke; Hartmut Maennel (CoRR, 2024)

Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties. - Hartmut Maennel; Oliver T. Unke; Klaus-Robert Müller (CoRR, 2024)

Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost. - J. Thorben Frank; Stefan Chmiela; Klaus-Robert Müller; Oliver T. Unke (CoRR, 2024)

Data from: The QCML dataset, Quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations. - Stefan Ganscha; Oliver T. Unke; Daniel Ahlin; Hartmut Maennel; Sergii Kashubin; Klaus-Robert Müller (Zenodo, 2025)

Data from: The QCML dataset, Quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations. - Stefan Ganscha; Oliver T. Unke; Daniel Ahlin; Hartmut Maennel; Sergii Kashubin; Klaus-Robert Müller (Zenodo, 2025)

Enhancing Diffusion Models Efficiency by Disentangling Total-Variance and Signal-to-Noise Ratio. - Khaled Kahouli; Winfried Ripken; Stefan Gugler; Oliver T. Unke; Klaus-Robert Müller; Shinichi Nakajima (CoRR, 2025)

How simple can you go? An off-the-shelf transformer approach to molecular dynamics. - Max Eissler; Tim Korjakow; Stefan Ganscha; Oliver T. Unke; Klaus-Robert Müller; Stefan Gugler (CoRR, 2025)

Sampling 3D Molecular Conformers with Diffusion Transformers. - J. Thorben Frank; Winfried Ripken; Gregor Lied; Klaus-Robert Müller; Oliver T. Unke; Stefan Chmiela (CoRR, 2025)