4 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…
Discretizing Group-Convolutional Neural Networks for 3D Geometry in Feature Space
Daniel Franzen, Jean Philip Filling, Michael Wand
Group-convolutional neural networks (GCNNs) are among the most important methods for introducing symmetry as an inductive bias in deep learning: In each linear layer, GCNNs sample…
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…