ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2101.03164
  4. Cited By
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

8 January 2021
Simon L. Batzner
Albert Musaelian
Lixin Sun
Mario Geiger
J. Mailoa
M. Kornbluth
N. Molinari
Tess E. Smidt
Boris Kozinsky
ArXivPDFHTML

Papers citing "E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials"

50 / 396 papers shown
Title
The Importance of Being Scalable: Improving the Speed and Accuracy of
  Neural Network Interatomic Potentials Across Chemical Domains
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
Eric Qu
Aditi S. Krishnapriyan
LRM
30
10
0
31 Oct 2024
Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
Kentaro Hino
Yuki Kurashige
34
0
0
31 Oct 2024
Does equivariance matter at scale?
Does equivariance matter at scale?
Johann Brehmer
S. Behrends
P. D. Haan
Taco S. Cohen
44
10
0
30 Oct 2024
Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds
  per Day
Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per Day
Jianxiong Li
Boyang Li
Zhuoqiang Guo
Mingzhen Li
Enji Li
Lijun Liu
Guojun Yuan
Zhan Wang
Guangming Tan
Weile Jia
AI4CE
40
1
0
30 Oct 2024
Pushing the Limits of All-Atom Geometric Graph Neural Networks:
  Pre-Training, Scaling and Zero-Shot Transfer
Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer
Zihan Pengmei
Zhengyuan Shen
Zichen Wang
Marcus Collins
Huzefa Rangwala
AI4CE
28
2
0
29 Oct 2024
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing
Julia Balla
S. Mishra-Sharma
C. Cuesta-Lázaro
Tommi Jaakkola
Tess E. Smidt
37
1
0
27 Oct 2024
Relaxed Equivariance via Multitask Learning
Relaxed Equivariance via Multitask Learning
Ahmed A. A. Elhag
T. Konstantin Rusch
Francesco Di Giovanni
Michael Bronstein
50
2
0
23 Oct 2024
JAMUN: Transferable Molecular Conformational Ensemble Generation with
  Walk-Jump Sampling
JAMUN: Transferable Molecular Conformational Ensemble Generation with Walk-Jump Sampling
Ameya Daigavane
Bodhi P. Vani
Saeed Saremi
J. Kleinhenz
Joshua Rackers
47
1
0
18 Oct 2024
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
Luis Barroso-Luque
Muhammed Shuaibi
Xiang Fu
Brandon M. Wood
Misko Dzamba
Meng Gao
Ammar Rizvi
C. L. Zitnick
Zachary W. Ulissi
AI4CE
PINN
41
16
0
16 Oct 2024
Are High-Degree Representations Really Unnecessary in Equivariant Graph
  Neural Networks?
Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
Jiacheng Cen
Anyi Li
Ning Lin
Yuxiang Ren
Zihe Wang
Wenbing Huang
43
2
0
15 Oct 2024
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property
  Prediction
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction
Longlong Li
Yipeng Zhang
Guanghui Wang
Kelin Xia
37
3
0
15 Oct 2024
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction
Junjie Xu
Artem Moskalev
Tommaso Mansi
Mangal Prakash
Rui Liao
AI4CE
26
1
0
15 Oct 2024
Towards Stable, Globally Expressive Graph Representations with Laplacian
  Eigenvectors
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors
Junru Zhou
Cai Zhou
Xiyuan Wang
Pan Li
Muhan Zhang
40
0
0
13 Oct 2024
Deconstructing equivariant representations in molecular systems
Deconstructing equivariant representations in molecular systems
Kin Long Kelvin Lee
Mikhail Galkin
Santiago Miret
30
2
0
10 Oct 2024
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum
  Properties for Improved ADMET Modeling
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling
Alessio Fallani
Ramil I. Nugmanov
Jose A. Arjona-Medina
Jörg Kurt Wegner
Alexandre Tkatchenko
Kostiantyn Chernichenko
MedIm
AI4CE
34
0
0
10 Oct 2024
Learning Equivariant Non-Local Electron Density Functionals
Learning Equivariant Non-Local Electron Density Functionals
Nicholas Gao
Eike Eberhard
Stephan Günnemann
28
1
0
10 Oct 2024
End-to-End Reaction Field Energy Modeling via Deep Learning based
  Voxel-to-voxel Transform
End-to-End Reaction Field Energy Modeling via Deep Learning based Voxel-to-voxel Transform
Yongxian Wu
Qiang Zhu
Ray Luo
21
0
0
04 Oct 2024
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups
Zakhar Shumaylov
Peter Zaika
James Rowbottom
Ferdia Sherry
Melanie Weber
Carola-Bibiane Schönlieb
41
1
0
03 Oct 2024
Deep Signature: Characterization of Large-Scale Molecular Dynamics
Deep Signature: Characterization of Large-Scale Molecular Dynamics
Tiexin Qin
Mengxu Zhu
Chunyang Li
Terry Lyons
Hong Yan
Haoliang Li
30
0
0
03 Oct 2024
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein
  Thermodynamics
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
Antonio Mirarchi
Raúl P. Peláez
Guillem Simeon
Gianni De Fabritiis
23
3
0
26 Sep 2024
Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric
  GNNs
Neural P3^33M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Yusong Wang
Chaoran Cheng
Shaoning Li
Yuxuan Ren
Bin Shao
Ge Liu
Pheng-Ann Heng
Nanning Zheng
AI4CE
35
3
0
26 Sep 2024
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph
  Neural Networks
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
Jiayu Peng
James K. Damewood
Jessica Karaguesian
Jaclyn R. Lunger
Rafael Gómez-Bombarelli
AI4CE
53
2
0
20 Sep 2024
Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic
  Potentials
Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic Potentials
Thomas Bischoff
Bastian Jäckl
Matthias Rupp
24
2
0
20 Sep 2024
Improving generalisability of 3D binding affinity models in low data
  regimes
Improving generalisability of 3D binding affinity models in low data regimes
Julia Buhmann
Ward Haddadin
Lukáš Pravda
Alan Bilsland
Hagen Triendl
AI4CE
28
0
0
19 Sep 2024
Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models
Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models
Alexius Wadell
Anoushka Bhutani
Venkatasubramanian Viswanathan
127
0
0
19 Sep 2024
Accelerating the Training and Improving the Reliability of
  Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials
  through Active Learning
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
Kisung Kang
Thomas A. R. Purcell
Christian Carbogno
Matthias Scheffler
AI4CE
32
0
0
18 Sep 2024
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
54
0
0
05 Sep 2024
On the design space between molecular mechanics and machine learning
  force fields
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang
Kenichiro Takaba
Michael S. Chen
Marcus Wieder
Yuzhi Xu
...
Kyunghyun Cho
Joe G. Greener
Peter K. Eastman
Stefano Martiniani
M. Tuckerman
AI4CE
42
4
0
03 Sep 2024
Towards Symbolic XAI -- Explanation Through Human Understandable Logical
  Relationships Between Features
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Thomas Schnake
Farnoush Rezaei Jafaria
Jonas Lederer
Ping Xiong
Shinichi Nakajima
Stefan Gugler
G. Montavon
Klaus-Robert Müller
43
4
0
30 Aug 2024
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
Paul Fuchs
Stephan Thaler
Sebastien Röcken
Julija Zavadlav
DiffM
79
6
0
28 Aug 2024
Latent Ewald summation for machine learning of long-range interactions
Latent Ewald summation for machine learning of long-range interactions
Bingqing Cheng
36
8
0
27 Aug 2024
Equivariant Reinforcement Learning under Partial Observability
Equivariant Reinforcement Learning under Partial Observability
Hai Nguyen
Andrea Baisero
David M. Klee
Dian Wang
Robert Platt
Christopher Amato
39
14
0
26 Aug 2024
Hessian QM9: A quantum chemistry database of molecular Hessians in
  implicit solvents
Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents
Nicholas J. Williams
Lara Kabalan
Ljiljana Stojanović
Viktor Zolyomi
Edward O. Pyzer-Knapp
34
3
0
15 Aug 2024
Impacts of floating-point non-associativity on reproducibility for HPC
  and deep learning applications
Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
Sanjif Shanmugavelu
Mathieu Taillefumier
Christopher Culver
Oscar Hernandez
Mark Coletti
Ada Sedova
35
2
0
09 Aug 2024
Advancing Molecular Machine (Learned) Representations with
  Stereoelectronics-Infused Molecular Graphs
Advancing Molecular Machine (Learned) Representations with Stereoelectronics-Infused Molecular Graphs
Daniil A. Boiko
Thiago Reschutzegger
Benjamín Sánchez-Lengeling
Samuel M. Blau
Gabe Gomes
GNN
AI4CE
34
1
0
08 Aug 2024
TASI Lectures on Physics for Machine Learning
TASI Lectures on Physics for Machine Learning
Jim Halverson
33
1
0
31 Jul 2024
Relaxed Equivariant Graph Neural Networks
Relaxed Equivariant Graph Neural Networks
E. Hofgard
Rui Wang
Robin Walters
Tess E. Smidt
40
1
0
30 Jul 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Makoto Takamoto
Viktor Zaverkin
Mathias Niepert
AI4CE
60
0
0
23 Jul 2024
${\it Asparagus}$: A Toolkit for Autonomous, User-Guided Construction of
  Machine-Learned Potential Energy Surfaces
Asparagus{\it Asparagus}Asparagus: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces
K. Töpfer
Luis Itza Vazquez-Salazar
Markus Meuwly
37
4
0
21 Jul 2024
Beyond Euclid: An Illustrated Guide to Modern Machine Learning with
  Geometric, Topological, and Algebraic Structures
Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures
Sophia Sanborn
Johan Mathe
Mathilde Papillon
Domas Buracas
Hansen Lillemark
Christian Shewmake
Abby Bertics
Xavier Pennec
Nina Miolane
58
4
0
12 Jul 2024
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine
  Learning Force Fields
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields
Shihao Shao
Haoran Geng
Zun Wang
Qinghua Cui
3DV
42
0
0
02 Jul 2024
Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks
Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks
Koki Chinzei
Shinichiro Yamano
Quoc-Hoan Tran
Yasuhiro Endo
H. Oshima
60
2
0
26 Jun 2024
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
Roman Bresson
Giannis Nikolentzos
G. Panagopoulos
Michail Chatzianastasis
Jun Pang
Michalis Vazirgiannis
69
42
0
26 Jun 2024
Probing the effects of broken symmetries in machine learning
Probing the effects of broken symmetries in machine learning
Marcel F. Langer
Sergey Pozdnyakov
Michele Ceriotti
AI4CE
41
7
0
25 Jun 2024
MatText: Do Language Models Need More than Text & Scale for Materials
  Modeling?
MatText: Do Language Models Need More than Text & Scale for Materials Modeling?
Nawaf Alampara
Santiago Miret
K. Jablonka
56
9
0
25 Jun 2024
GeoMFormer: A General Architecture for Geometric Molecular
  Representation Learning
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen
Shengjie Luo
Di He
Shuxin Zheng
Tie-Yan Liu
Liwei Wang
AI4CE
38
5
0
24 Jun 2024
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for
  Physical Dynamics Learning
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
Zinan Zheng
Yang Liu
Jia Li
Jianhua Yao
Yu Rong
AI4CE
51
1
0
24 Jun 2024
NeuralSCF: Neural network self-consistent fields for density functional
  theory
NeuralSCF: Neural network self-consistent fields for density functional theory
Feitong Song
Ji Feng
44
0
0
22 Jun 2024
Molecule Graph Networks with Many-body Equivariant Interactions
Molecule Graph Networks with Many-body Equivariant Interactions
Zetian Mao
Jiawen Li
Chen Liang
Diptesh Das
Masato Sumita
Koji Tsuda
Kelin Xia
Koji Tsuda
35
1
0
19 Jun 2024
Efficient mapping of phase diagrams with conditional Boltzmann
  Generators
Efficient mapping of phase diagrams with conditional Boltzmann Generators
Maximilian Schebek
Michele Invernizzi
Frank Noé
Jutta Rogal
45
8
0
18 Jun 2024
Previous
12345678
Next