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2101.03164
Cited By
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
8 January 2021
Simon L. Batzner
Albert Musaelian
Lixin Sun
Mario Geiger
J. Mailoa
M. Kornbluth
N. Molinari
Tess E. Smidt
Boris Kozinsky
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Papers citing
"E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials"
7 / 7 papers shown
Title
Learning simple heuristic rules for classifying materials based on chemical composition
Andrew Ma
Marin Soljacic
15
0
0
05 May 2025
Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework Without Data
Maximilian Stupp
P. S. Koutsourelakis
33
0
0
29 Apr 2025
MatterChat: A Multi-Modal LLM for Material Science
Yingheng Tang
Wenbin Xu
Jie Cao
Jianzhu Ma
Weilu Gao
Steve Farrell
Benjamin Erichson
Michael W. Mahoney
Andy Nonaka
95
3
0
18 Feb 2025
Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors
Qiangqiang Gu
S. K. Pandey
Zhanghao Zhouyin
45
0
0
02 Feb 2025
Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
Kentaro Hino
Yuki Kurashige
24
0
0
31 Oct 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Makoto Takamoto
Viktor Zaverkin
Mathias Niepert
AI4CE
22
0
0
23 Jul 2024
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Oliver T. Unke
Stefan Chmiela
M. Gastegger
Kristof T. Schütt
H. E. Sauceda
K. Müller
142
192
0
01 May 2021
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