8 citations · 10 across the 5 of their papers we have counts for
5 papers
Learning Inter-Atomic Potentials without Explicit Equivariance
Ahmed A. Elhag, Arun Raja, Alex Morehead +6
Accurate and scalable machine-learned inter-atomic potentials (MLIPs) are essential for molecular simulations ranging from drug discovery to new material design. Current state-of-t…
Relaxed Equivariance via Multitask Learning
Ahmed A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni +1
Incorporating equivariance as an inductive bias into deep learning architectures to take advantage of the data symmetry has been successful in multiple applications, such as chemis…
Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
Yuyang Wang, Ahmed A. Elhag, Navdeep Jaitly +2
We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the heuristics of prior works and achieves state of the art results by us…
Manifold Diffusion Fields
Ahmed A. Elhag, Yuyang Wang, Joshua M. Susskind +1
We present Manifold Diffusion Fields (MDF), an approach that unlocks learning of diffusion models of data in general non-Euclidean geometries. Leveraging insights from spectral geo…
Graph Anisotropic Diffusion
Ahmed A. A. Elhag, Gabriele Corso, Hannes Stärk +1
Traditional Graph Neural Networks (GNNs) rely on message passing, which amounts to permutation-invariant local aggregation of neighbour features. Such a process is isotropic and th…