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Relevance of Rotationally Equivariant Convolutions for Predicting
  Molecular Properties
v1v2v3v4 (latest)

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties

19 August 2020
Benjamin Kurt Miller
Mario Geiger
Tess E. Smidt
Frank Noé
ArXiv (abs)PDFHTML

Papers citing "Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties"

50 / 52 papers shown
Equivariant Spherical Transformer for Efficient Molecular Modeling
Equivariant Spherical Transformer for Efficient Molecular Modeling
Junyi An
Xinyu Lu
Chao Qu
Yunfei Shi
Peijia Lin
Qianwei Tang
Licheng Xu
Fenglei Cao
Yuan Qi
342
3
0
29 May 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron J. Havens
Benjamin Kurt Miller
Bing Yan
Carles Domingo-Enrich
Anuroop Sriram
...
Brandon Amos
Brian Karrer
Xiang Fu
Guan-Horng Liu
Ricky T. Q. Chen
DiffM
675
52
0
16 Apr 2025
Permutation Equivariant Neural Networks for Symmetric Tensors
Permutation Equivariant Neural Networks for Symmetric Tensors
Edward Pearce-Crump
499
2
0
14 Mar 2025
FlowLLM: Flow Matching for Material Generation with Large Language
  Models as Base Distributions
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base DistributionsNeural Information Processing Systems (NeurIPS), 2024
Anuroop Sriram
Benjamin Kurt Miller
Ricky T. Q. Chen
Brandon M. Wood
AI4CE
285
56
0
30 Oct 2024
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic
  Interpolants
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
Allan dos Santos Costa
Ilan Mitnikov
Franco Pellegrini
Ameya Daigavane
Mario Geiger
Zhonglin Cao
Karsten Kreis
Tess E. Smidt
E. Küçükbenli
J. Jacobson
319
14
0
12 Oct 2024
Molecule Graph Networks with Many-body Equivariant Interactions
Molecule Graph Networks with Many-body Equivariant InteractionsJournal of Chemical Theory and Computation (JCTC), 2024
Zetian Mao
Jiawen Li
Chen Liang
Chen Liang
Diptesh Das
Masato Sumita
Kelin Xia
Koji Tsuda
464
8
0
19 Jun 2024
FlowMM: Generating Materials with Riemannian Flow Matching
FlowMM: Generating Materials with Riemannian Flow MatchingInternational Conference on Machine Learning (ICML), 2024
Benjamin Kurt Miller
Ricky T. Q. Chen
Anuroop Sriram
Brandon M. Wood
389
96
0
07 Jun 2024
Generalizing Denoising to Non-Equilibrium Structures Improves
  Equivariant Force Fields
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
Yi-Lun Liao
Tess E. Smidt
Abhishek Das
DiffMAI4CE
211
26
0
14 Mar 2024
Accelerating superconductor discovery through tempered deep learning of
  the electron-phonon spectral function
Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function
Jason B. Gibson
A. Hire
P. M. Dee
Oscar Barrera
Benjamin Geisler
P. Hirschfeld
R. G. Hennig
209
14
0
29 Jan 2024
Diffusion-Driven Generative Framework for Molecular Conformation
  Prediction
Diffusion-Driven Generative Framework for Molecular Conformation Prediction
Bobin Yang
Jie Deng
Zhenghan Chen
Ruoxue Wu
DiffM
351
0
0
22 Dec 2023
Symmetry-enforcing neural networks with applications to constitutive
  modeling
Symmetry-enforcing neural networks with applications to constitutive modeling
Kévin Garanger
Julie A Kraus
J. Rimoli
286
14
0
21 Dec 2023
On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space
Zihan Zhou
Ruiying Liu
Tianshu Yu
DiffM
263
0
0
07 Oct 2023
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical
  Coarse-graining SO(3)-Equivariant Autoencoders
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders
Allan dos Santos Costa
Ilan Mitnikov
Mario Geiger
Manvitha Ponnapati
Tess E. Smidt
Joseph Jacobson
DiffM
352
5
0
04 Oct 2023
Molecular Conformation Generation via Shifting Scores
Molecular Conformation Generation via Shifting Scores
Zihan Zhou
Ruiying Liu
Chaolong Ying
Ruimao Zhang
Tianshu Yu
DiffM
294
2
0
12 Sep 2023
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural
  Networks
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
Daniel Levy
Sekouba Kaba
Carmelo Gonzales
Santiago Miret
Siamak Ravanbakhsh
213
9
0
06 Sep 2023
StrainTensorNet: Predicting crystal structure elastic properties using
  SE(3)-equivariant graph neural networks
StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networksPhysical Review Research (Phys. Rev. Res.), 2023
T. Pakornchote
A. Ektarawong
Thiparat Chotibut
169
6
0
22 Jun 2023
EquiformerV2: Improved Equivariant Transformer for Scaling to
  Higher-Degree Representations
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree RepresentationsInternational Conference on Learning Representations (ICLR), 2023
Yidong Liao
Brandon M. Wood
Abhishek Das
Tess E. Smidt
560
301
0
21 Jun 2023
Task-Equivariant Graph Few-shot Learning
Task-Equivariant Graph Few-shot LearningKnowledge Discovery and Data Mining (KDD), 2023
Sungwon Kim
Junseok Lee
Namkyeong Lee
Wonjoon Kim
Seung-Deok Choi
Chanyoung Park
555
12
0
30 May 2023
Geometric Deep Learning for Molecular Crystal Structure Prediction
Geometric Deep Learning for Molecular Crystal Structure PredictionJournal of Chemical Theory and Computation (JCTC), 2023
Michael Kilgour
Jutta Rogal
M. Tuckerman
243
27
0
17 Mar 2023
Geometric Clifford Algebra Networks
Geometric Clifford Algebra NetworksInternational Conference on Machine Learning (ICML), 2023
David Ruhe
Jayesh K. Gupta
Steven De Keninck
Max Welling
Johannes Brandstetter
AI4CE
414
57
0
13 Feb 2023
INO: Invariant Neural Operators for Learning Complex Physical Systems
  with Momentum Conservation
INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum ConservationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Ning Liu
Yue Yu
Huaiqian You
Neeraj Tatikola
AI4CE
276
33
0
29 Dec 2022
Implicit Convolutional Kernels for Steerable CNNs
Implicit Convolutional Kernels for Steerable CNNsNeural Information Processing Systems (NeurIPS), 2022
Maksim Zhdanov
Nico Hoffmann
Gabriele Cesa
444
13
0
12 Dec 2022
Learning Regularized Positional Encoding for Molecular Prediction
Learning Regularized Positional Encoding for Molecular Prediction
Yantao Du
Weihao Gao
Wen Xiao
Zhirui Wang
Chong Wang
Liang Xiang
AI4CE
200
2
0
23 Nov 2022
Hierarchical Learning in Euclidean Neural Networks
Hierarchical Learning in Euclidean Neural Networks
Joshua A. Rackers
P. Rao
149
1
0
10 Oct 2022
Machine learning frontier orbital energies of nanodiamonds
Machine learning frontier orbital energies of nanodiamondsJournal of Chemical Theory and Computation (JCTC), 2022
Thorren Kirschbaum
B. V. Seggern
J. Dzubiella
A. Bande
Frank Noé
AI4CE
247
7
0
30 Sep 2022
Machine learning and invariant theory
Machine learning and invariant theoryNotices of the American Mathematical Society (Notices of the AMS), 2022
Ben Blum-Smith
Soledad Villar
AI4CE
366
27
0
29 Sep 2022
Graph Neural Networks for Molecules
Graph Neural Networks for Molecules
Yuyang Wang
Zijie Li
A. Farimani
GNNAI4CE
400
61
0
12 Sep 2022
Ab-initio quantum chemistry with neural-network wavefunctions
Ab-initio quantum chemistry with neural-network wavefunctionsNature Reviews Chemistry (Nat. Rev. Chem.), 2022
J. Hermann
J. Spencer
Kenny Choo
Antonio Mezzacapo
W. Foulkes
David Pfau
Giuseppe Carleo
Frank Noé
AI4CE
212
123
0
26 Aug 2022
e3nn: Euclidean Neural Networks
e3nn: Euclidean Neural Networks
Mario Geiger
Tess E. Smidt
246
262
0
18 Jul 2022
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic
  Graphs
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic GraphsInternational Conference on Learning Representations (ICLR), 2022
Yi-Lun Liao
Tess E. Smidt
471
353
0
23 Jun 2022
Pre-training via Denoising for Molecular Property Prediction
Pre-training via Denoising for Molecular Property PredictionInternational Conference on Learning Representations (ICLR), 2022
Sheheryar Zaidi
Michael Schaarschmidt
James Martens
Hyunjik Kim
Yee Whye Teh
Alvaro Sanchez-Gonzalez
Peter W. Battaglia
Razvan Pascanu
Jonathan Godwin
DiffMAI4CE
542
161
0
31 May 2022
Transferring Chemical and Energetic Knowledge Between Molecular Systems
  with Machine Learning
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine LearningCommunications Chemistry (Commun. Chem.), 2022
Sajjad Heydari
S. Raniolo
L. Livi
V. Limongelli
254
5
0
06 May 2022
Learning Local Equivariant Representations for Large-Scale Atomistic
  Dynamics
Learning Local Equivariant Representations for Large-Scale Atomistic DynamicsNature Communications (Nat Commun), 2022
Albert Musaelian
Simon L. Batzner
A. Johansson
Lixin Sun
Cameron J. Owen
M. Kornbluth
Boris Kozinsky
356
701
0
11 Apr 2022
GeoDiff: a Geometric Diffusion Model for Molecular Conformation
  Generation
GeoDiff: a Geometric Diffusion Model for Molecular Conformation GenerationInternational Conference on Learning Representations (ICLR), 2022
Minkai Xu
Lantao Yu
Yang Song
Chence Shi
Stefano Ermon
Jian Tang
BDLDiffM
636
675
0
06 Mar 2022
Equivariant Graph Attention Networks for Molecular Property Prediction
Equivariant Graph Attention Networks for Molecular Property Prediction
Tuan Le
Frank Noé
Djork-Arné Clevert
298
28
0
20 Feb 2022
Unsupervised Learning of Group Invariant and Equivariant Representations
Unsupervised Learning of Group Invariant and Equivariant RepresentationsNeural Information Processing Systems (NeurIPS), 2022
R. Winter
Marco Bertolini
Tuan Le
Frank Noé
Djork-Arné Clevert
324
53
0
15 Feb 2022
Relative Molecule Self-Attention Transformer
Relative Molecule Self-Attention TransformerJournal of Cheminformatics (J Cheminform), 2021
Lukasz Maziarka
Dawid Majchrowski
Tomasz Danel
Piotr Gaiñski
Jacek Tabor
Igor T. Podolak
Pawel M. Morkisz
Stanislaw Jastrzebski
MedIm
322
46
0
12 Oct 2021
Geometric and Physical Quantities Improve E(3) Equivariant Message
  Passing
Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
Johannes Brandstetter
Rob D. Hesselink
Elise van der Pol
Erik J. Bekkers
Max Welling
545
301
0
06 Oct 2021
Applying Machine Learning to Study Fluid Mechanics
Applying Machine Learning to Study Fluid Mechanics
Steven L. Brunton
PINNAI4CE
187
120
0
05 Oct 2021
Federated Learning of Molecular Properties with Graph Neural Networks in
  a Heterogeneous Setting
Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting
Wei-wei Zhu
Jiebo Luo
Andrew D. White
FedML
366
49
0
15 Sep 2021
Geometric Deep Learning on Molecular Representations
Geometric Deep Learning on Molecular RepresentationsNature Machine Intelligence (Nat. Mach. Intell.), 2021
Kenneth Atz
F. Grisoni
G. Schneider
AI4CE
593
375
0
26 Jul 2021
Rotation Invariant Graph Neural Networks using Spin Convolutions
Rotation Invariant Graph Neural Networks using Spin Convolutions
Muhammed Shuaibi
Adeesh Kolluru
Abhishek Das
Aditya Grover
Anuroop Sriram
Zachary W. Ulissi
C. L. Zitnick
AI4CE
318
82
0
17 Jun 2021
Flexible dual-branched message passing neural network for quantum
  mechanical property prediction with molecular conformation
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
Jeonghee Jo
Bumju Kwak
Byunghan Lee
Sungroh Yoon
183
2
0
14 Jun 2021
Optimal radial basis for density-based atomic representations
Optimal radial basis for density-based atomic representationsJournal of Chemical Physics (JCP), 2021
Alexander Goscinski
Félix Musil
Sergey Pozdnyakov
Michele Ceriotti
244
19
0
18 May 2021
Learning Gradient Fields for Molecular Conformation Generation
Learning Gradient Fields for Molecular Conformation GenerationInternational Conference on Machine Learning (ICML), 2021
Chence Shi
Shitong Luo
Minkai Xu
Jian Tang
DiffMAI4CE
492
246
0
09 May 2021
E(n) Equivariant Graph Neural Networks
E(n) Equivariant Graph Neural NetworksInternational Conference on Machine Learning (ICML), 2021
Victor Garcia Satorras
Emiel Hoogeboom
Max Welling
693
1,387
0
19 Feb 2021
Equivariant message passing for the prediction of tensorial properties
  and molecular spectra
Equivariant message passing for the prediction of tensorial properties and molecular spectraInternational Conference on Machine Learning (ICML), 2021
Kristof T. Schütt
Oliver T. Unke
M. Gastegger
441
724
0
05 Feb 2021
LieTransformer: Equivariant self-attention for Lie Groups
LieTransformer: Equivariant self-attention for Lie GroupsInternational Conference on Machine Learning (ICML), 2020
M. Hutchinson
Charline Le Lan
Sheheryar Zaidi
Emilien Dupont
Yee Whye Teh
Hyunjik Kim
748
132
0
20 Dec 2020
Symmetry-Aware Actor-Critic for 3D Molecular Design
Symmetry-Aware Actor-Critic for 3D Molecular DesignInternational Conference on Learning Representations (ICLR), 2020
G. Simm
Tian Xie
Gábor Csányi
José Miguel Hernández-Lobato
AI4CE
279
72
0
25 Nov 2020
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
The Open Catalyst 2020 (OC20) Dataset and Community ChallengesACS Catalysis (ACS Catal.), 2020
L. Chanussot
Abhishek Das
Siddharth Goyal
Thibaut Lavril
Muhammed Shuaibi
...
Brandon M. Wood
Junwoong Yoon
Devi Parikh
C. L. Zitnick
Zachary W. Ulissi
789
747
0
20 Oct 2020
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