1.6k citations · 7.4k across the 26 of their papers we have counts for
28 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations
Johannes Maeß, Leon Werner, J. Thorben Frank +5
We introduce implicit machine learning force fields (I-MLFFs), which replace explicit stacks of neural network layers with self-consistent fixed-point equations. In molecular simul…
How Atoms Interact Within Molecules
Adil Kabylda, Malte Esders, Matteo Gori +3
Fundamental understanding of interatomic forces in molecules must emerge from quantum mechanics, yet widely used empirical force fields rely on simplified mechanistic approximation…
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…
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…
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…