8 papers
Ab initio parametrization of distributed polarizable force fields
Felix Post, Jean-Philip Filling, Toulik Maitra +3
Polarizable force fields offer superior transferability and accuracy compared to classical force fields, enabling access to electronic response properties such as refractive index…
Tensor Channel Equivariant Graph Neural Networks for Molecular Polarizability Prediction
Jean Philip Filling, Daniel Franzen, Michael Wand
We introduce a tensor-channel equivariant graph neural network for direct prediction of molecular polarizability tensors. Building on the efficient PaiNN architecture, we augment t…
Reconstruction of spin structures from topological charge distributions via generative neural network systems
Kyra H. M. Klos, Jan Disselhoff, Michael Wand +2
Localized topological defects inherently possess a multiscale character. While their microstructure configuration depends on the specific physical system, their topological feature…
Multiple Token Divergence: Measuring and Steering In-Context Computation Density
Vincent Herrmann, Eric Alcaide, Michael Wand +1
Measuring the in-context computational effort of language models is a key challenge, as metrics like next-token loss fail to capture reasoning complexity. Prior methods based on la…
Space Explanations of Neural Network Classification
Faezeh Labbaf, Tomáš Kolárik, Martin Blicha +3
We present a novel logic-based concept called Space Explanations for classifying neural networks that gives provable guarantees of the behavior of the network in continuous areas o…
Direct Molecular Polarizability Prediction with SO(3) Equivariant Local Frame GNNs
Jean Philip Filling, Felix Post, Michael Wand +1
We introduce a novel equivariant graph neural network (GNN) architecture designed to predict the tensorial response properties of molecules. Unlike traditional frameworks that focu…