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Found 10 papers, 5 papers with code
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 #5 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.
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy.
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.
Physics-inspired structural representations for molecules and materials
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation.
Chemical Physics
Sensitivity and Dimensionality of Atomic Environment Representations used for Machine Learning Interatomic Potentials
Faithfully representing chemical environments is essential for describing materials and molecules with machine learning approaches.
Materials Science Chemical Physics
Atomic Cluster Expansion: Completeness, Efficiency and Stability
The Atomic Cluster Expansion (Drautz, Phys.
Numerical Analysis Numerical Analysis
