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Found 10 papers, 6 papers with code
Zero Shot Molecular Generation via Similarity Kernels
Using insights from the trained model, we present Similarity-based Molecular Generation (SiMGen), a new method for zero shot molecular generation.
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
In this work, we generate a big dataset of structure-property relationships for strut-based lattices.
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds
To any coordinate-independent feature field on a manifold comes attached an equivariant embedding of the principal bundle to the space of numerical features.
Equivariant Matrix Function Neural Networks
Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications.
Ranked #3 on
Graph Regression
on ZINC-500k
Retrieval of Boost Invariant Symbolic Observables via Feature Importance
Deep learning approaches for jet tagging in high-energy physics are characterized as black boxes that process a large amount of information from which it is difficult to extract key distinctive observables.
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science
We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies.
Tensor-reduced atomic density representations
Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them.
BIP: Boost Invariant Polynomials for Efficient Jet Tagging
Deep Learning approaches are becoming the go-to methods for data analysis in High Energy Physics (HEP).
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
In particular, we show that using four-body messages reduces the required number of message passing iterations to just two, resulting in a fast and highly parallelizable model, reaching or exceeding state-of-the-art accuracy on the rMD17, 3BPA, and AcAc benchmark tasks.
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures.
