1.6k citations · 3.6k across the 26 of their papers we have counts for
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Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
Winfried Ripken, Michael Plainer, Gregor Lied +5
Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome this constraint, we introduce a f…
Control Variate Score Matching for Diffusion Models
Khaled Kahouli, Romuald Elie, Klaus-Robert Müller +3
Sampling from unnormalized probability densities is a pervasive challenge across the computational and physical sciences. Diffusion models provide a powerful generative framework f…
Sampling 3D Molecular Conformers with Diffusion Transformers
J. Thorben Frank, Winfried Ripken, Gregor Lied +3
Diffusion Transformers (DiTs) have demonstrated strong performance in generative modeling, particularly in image synthesis, making them a compelling choice for molecular conformer…
How simple can you go? An off-the-shelf transformer approach to molecular dynamics
Max Eissler, Tim Korjakow, Stefan Ganscha +3
Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other ma…
Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
Khaled Kahouli, Winfried Ripken, Stefan Gugler +3
Diffusion models have been widely adopted as a powerful generative framework across numerous scientific domains, yet their inherently iterative sampling process remains a significa…
Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost
J. Thorben Frank, Stefan Chmiela, Klaus-Robert Müller +1
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of dist…
Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties
Hartmut Maennel, Oliver T. Unke, Klaus-Robert Müller
When modeling physical properties of molecules with machine learning, it is desirable to incorporate -covariance. While such models based on low body order features are not…
E3x: -Equivariant Deep Learning Made Easy
Oliver T. Unke, Hartmut Maennel
This work introduces E3x, a software package for building neural networks that are equivariant with respect to the Euclidean group , consisting of translations, rota…
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller
The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-ini…
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof T. Schütt, Oliver T. Unke, Michael Gastegger
Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamic…