Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

This paper introduces a new model to learn graph neural networks equivariant to rotations,
translations, reflections and permutations called E (n)-Equivariant Graph Neural Networks …

Effectively predicting molecular interactions has the potential to accelerate molecular
dynamics by multiple orders of magnitude and thus revolutionize chemical simulations …

The widespread use of neural networks across different scientific domains often involves
constraining them to satisfy certain symmetries, conservation laws, or other domain …

Message-passing neural networks (MPNNs) have been successfully applied to
representation learning on graphs in a variety of real-world applications. However, two …

Learning representations of sets of nodes in a graph is crucial for applications ranging from
node-role discovery to link prediction and molecule classification. Graph Neural Networks …

Advancements in neural machinery have led to a wide range of algorithmic solutions for
molecular property prediction. Two classes of models in particular have yielded promising …

Abstract Message passing Graph Neural Networks (GNNs) provide a powerful modeling
framework for relational data. However, the expressive power of existing GNNs is upper …

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …