34 citations · 37 across the 7 of their papers we have counts for
6 papers · 1 filter
UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu +15
The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science includi…
Does equivariance matter at scale?
Johann Brehmer, Sönke Behrends, Pim de Haan +1
Given large datasets and sufficient compute, is it beneficial to design neural architectures for the structure and symmetries of each problem? Or is it more efficient to learn them…
FoMo Rewards: Can we cast foundation models as reward functions?
Ekdeep Singh Lubana, Johann Brehmer, Pim de Haan +1
We explore the viability of casting foundation models as generic reward functions for reinforcement learning. To this end, we propose a simple pipeline that interfaces an off-the-s…
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi +7
Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euc…
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
Pim de Haan, Taco Cohen, Johann Brehmer
The Geometric Algebra Transformer (GATr) is a versatile architecture for geometric deep learning based on projective geometric algebra. We generalize this architecture into a bluep…
Natural Graph Networks
Pim de Haan, Taco Cohen, Max Welling
A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described. Traditionally this has been taken to mean…