4 citations · 7 across the 5 of their papers we have counts for
5 papers
BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
Lars L. Schaaf, Ilyes Batatia, Christoph Brunken +2
Simulating atomic-scale processes, such as protein dynamics and catalytic reactions, is crucial for advancements in biology, chemistry, and materials science. Machine learning forc…
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
Wojciech G. Stark, Cas van der Oord, Ilyes Batatia +4
Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynami…
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
Ivan Grega, Ilyes Batatia, Gábor Csányi +2
Lattices are architected metamaterials whose properties strongly depend on their geometrical design. The analogy between lattices and graphs enables the use of graph neural network…
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds
Ilyes Batatia
This work proposes a geometric insight into equivariant message passing on Riemannian manifolds. As previously proposed, numerical features on Riemannian manifolds are represented…
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
Ilyes Batatia, Mario Geiger, Jose Munoz +3
Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quan…