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DOAJ Open Access 2022

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Simon Batzner Albert Musaelian Lixin Sun Mario Geiger Jonathan P. Mailoa +4 lainnya
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Abstrak

An E(3)-equivariant deep learning interatomic potential is introduced for accelerating molecular dynamics simulations. The method obtains state-of-the-art accuracy, can faithfully describe dynamics of complex systems with remarkable sample efficiency.

Topik & Kata Kunci

Science

Penulis (9)

S

Simon Batzner

A

Albert Musaelian

L

Lixin Sun

M

Mario Geiger

J

Jonathan P. Mailoa

M

Mordechai Kornbluth

N

Nicola Molinari

T

Tess E. Smidt

B

Boris Kozinsky

Format Sitasi

APA MLA BibTeX
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J.P., Kornbluth, M. et al. (2022). E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. https://doi.org/10.1038/s41467-022-29939-5

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Lihat di Sumber doi.org/10.1038/s41467-022-29939-5
Informasi Jurnal
Tahun Terbit
2022
Sumber Database
DOAJ
DOI
10.1038/s41467-022-29939-5
Akses
Open Access ✓