10 papers
Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations
Johannes MaeÃ, 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…
Enhancing molecular dynamics with equivariant machine-learned densities
Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto +3
Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving elect…
OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
Maaike Galama, Nina Kozar-Gillan, Christina Embacher +18
The tumor microenvironment (TME) plays a central role in cancer progression, treatment response, and patient outcomes, yet large-scale, consistent, and quantitative TME characteriz…
Characterizing High-Capacity Janus Aminobenzene-Graphene Anode for Sodium-Ion Batteries with Machine Learning
Claudia Islas-Vargas, L. Ricardo Montoya, Carlos A. Vital-José +3
Sodium-ion batteries require anodes that combine high capacity, low operating voltage, fast Na-ion transport, and mechanical stability, which conventional anodes struggle to delive…
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…
Manipulating Feature Visualizations with Gradient Slingshots
Dilyara Bareeva, Marina M. -C. Höhne, Alexander Warnecke +5
Feature Visualization (FV) is a widely used technique for interpreting concepts learned by Deep Neural Networks (DNNs), which synthesizes input patterns that maximally activate a g…