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Efficient Approximations of Complete Interatomic Potentials for Crystal
  Property Prediction

Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction

12 June 2023
Yu-Ching Lin
Keqiang Yan
Youzhi Luo
Yi Liu
Xiaoning Qian
Shuiwang Ji
ArXivPDFHTML

Papers citing "Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction"

6 / 6 papers shown
Title
Ewald-based Long-Range Message Passing for Molecular Graphs
Ewald-based Long-Range Message Passing for Molecular Graphs
Arthur Kosmala
Johannes Gasteiger
Nicholas Gao
Stephan Günnemann
64
14
0
08 Mar 2023
Periodic Graph Transformers for Crystal Material Property Prediction
Periodic Graph Transformers for Crystal Material Property Prediction
Keqiang Yan
Yi Liu
Yu-Ching Lin
Shuiwang Ji
AI4TS
60
58
0
23 Sep 2022
Scalable deeper graph neural networks for high-performance materials
  property prediction
Scalable deeper graph neural networks for high-performance materials property prediction
Sadman Sadeed Omee
Steph-Yves M. Louis
Nihang Fu
Lai Wei
Sourin Dey
Rongzhi Dong
Qinyang Li
Jianjun Hu
62
49
0
25 Sep 2021
GraphDF: A Discrete Flow Model for Molecular Graph Generation
GraphDF: A Discrete Flow Model for Molecular Graph Generation
Youzhi Luo
Keqiang Yan
Shuiwang Ji
DRL
142
152
0
01 Feb 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
175
860
0
08 Jan 2021
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
187
1,205
0
12 Feb 2018
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