249 citations · 260 across the 10 of their papers we have counts for
3 papers · 1 filter
A Theoretical Case Study of the Generalisation of Machine-learned Potentials
Yangshuai Wang, Shashwat Patel, Christoph Ortner
Machine-learned interatomic potentials (MLIPs) are typically trained on datasets that encompass a restricted subset of possible input structures, which presents a potential challen…
Equivariant Matrix Function Neural Networks
Ilyes Batatia, Lars L. Schaaf, Huajie Chen +3
Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications. However, MP…
ACEpotentials.jl: A Julia Implementation of the Atomic Cluster Expansion
William C. Witt, Cas van der Oord, Elena Gelžinytė +10
We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion (D…