Cargando…
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act...
Autores principales: | Batzner, Simon, Musaelian, Albert, Sun, Lixin, Geiger, Mario, Mailoa, Jonathan P., Kornbluth, Mordechai, Molinari, Nicola, Smidt, Tess E., Kozinsky, Boris |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9068614/ https://www.ncbi.nlm.nih.gov/pubmed/35508450 http://dx.doi.org/10.1038/s41467-022-29939-5 |
Ejemplares similares
-
Learning local equivariant representations for large-scale atomistic dynamics
por: Musaelian, Albert, et al.
Publicado: (2023) -
Equivariant
Graph Neural Networks for Toxicity Prediction
por: Cremer, Julian, et al.
Publicado: (2023) -
E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction
por: Roche, Rahmatullah, et al.
Publicado: (2023) -
An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
por: Gong, Shiqi, et al.
Publicado: (2022) -
3D-equivariant graph neural networks for protein model quality assessment
por: Chen, Chen, et al.
Publicado: (2023)