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Spin-Dependent Graph Neural Network Potential for Magnetic Materials

Spin-Dependent Graph Neural Network Potential for Magnetic Materials

6 March 2022
Hongyu Yu
Yang Zhong
Liangliang Hong
Changsong Xu
W. Ren
X. Gong
Hongjun Xiang
ArXivPDFHTML

Papers citing "Spin-Dependent Graph Neural Network Potential for Magnetic Materials"

5 / 5 papers shown
Title
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs
D. Morgan
Siamak Attarian
Jun Meng
Chen Shen
...
K. J. Schmidt
So Takamoto
Aidan Thompson
Julia Westermayr
Brandon M. Wood
55
4
0
12 Mar 2025
SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of
  Freedom with Multi-Task Learning
SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of Freedom with Multi-Task Learning
Koki Ueno
Satoru Ohuchi
Kazuhide Ichikawa
Kei Amii
Kensuke Wakasugi
48
0
0
05 Sep 2024
Crystal Diffusion Variational Autoencoder for Periodic Material
  Generation
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Tian Xie
Xiang Fu
O. Ganea
Regina Barzilay
Tommi Jaakkola
DiffM
BDL
212
230
0
12 Oct 2021
Complex Spin Hamiltonian Represented by Artificial Neural Network
Complex Spin Hamiltonian Represented by Artificial Neural Network
Hongyu Yu
Changsong Xu
Feng Lou
L. Bellaiche
Zhenpeng Hu
X. Gong
H. Xiang
26
15
0
02 Oct 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate
  Interatomic Potentials
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon L. Batzner
Albert Musaelian
Lixin Sun
Mario Geiger
J. Mailoa
M. Kornbluth
N. Molinari
Tess E. Smidt
Boris Kozinsky
203
1,238
0
08 Jan 2021
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