36 citations · 84 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions
Alvaro Carbonero, Alexandre Duval, Victor Schmidt +4
The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all a…
Crystal-GFN: sampling crystals with desirable properties and constraints
Mila AI4Science, :, Alex Hernandez-Garcia +11
The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challe…
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia +4
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries. While graph neural netwo…