Cormorant: Covariant Molecular Neural Networks

6 June 2019

Papers citing "Cormorant: Covariant Molecular Neural Networks"

50 / 250 papers shown

Title
Symmetry-preserving graph attention network to solve routing problems at multiple resolutions Cong Dao Tran Thong Bach Truong Son-Hy 31 0 0 24 Oct 2023
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds Ilyes Batatia 23 0 0 16 Oct 2023
Equivariant Matrix Function Neural Networks Ilyes Batatia Lars L. Schaaf Huajie Chen Gábor Csányi Christoph Ortner Felix A. Faber 32 5 0 16 Oct 2023
Fast, Expressive SE $(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space Erik J. Bekkers Sharvaree P. Vadgama Rob D. Hesselink P. A. V. D. Linden David W. Romero 13 24 0 04 Oct 2023
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution Rui Wang E. Hofgard Han Gao Robin Walters Tess E. Smidt AI4CE 32 9 0 03 Oct 2023
Learning to Transform for Generalizable Instance-wise Invariance Yan Liu Carlos Esteves Franccois Marelli Stella X. Yu OOD 30 1 0 28 Sep 2023
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields J. Frank Oliver T. Unke Klaus-Robert Muller Stefan Chmiela 26 3 0 21 Sep 2023
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power Junru Zhou Jiarui Feng Xiyuan Wang Muhan Zhang 24 8 0 10 Sep 2023
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks Daniel Levy Sekouba Kaba Carmelo Gonzales Santiago Miret Siamak Ravanbakhsh 38 5 0 06 Sep 2023
Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning Haoxiang Luo Gang Sun 34 2 0 02 Aug 2023
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds O. Melia Eric Jonas Rebecca Willett OOD 3DPC 18 3 0 27 Jul 2023
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules Haiyang Yu Meng Liu Youzhi Luo A. Strasser X. Qian Xiaoning Qian Shuiwang Ji 15 20 0 15 Jun 2023
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials Guillem Simeon Gianni de Fabritiis 26 45 0 10 Jun 2023
Scaling Spherical CNNs Carlos Esteves Jean-Jacques E. Slotine A. Makadia GNN LRM 19 14 0 08 Jun 2023
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian Haiyang Yu Zhao Xu X. Qian Xiaoning Qian Shuiwang Ji 37 24 0 08 Jun 2023
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks Mohannad Elhamod Mridul Khurana Harish Babu Manogaran Josef C. Uyeda M. Balk ... Wei-Lun Chao Chuck Stewart Daniel Rubenstein T. Berger-Wolf Anuj Karpatne 14 7 0 05 Jun 2023
Group Invariant Global Pooling Kamil Bujel Yonatan Gideoni Chaitanya K. Joshi Pietro Lio' 37 0 0 30 May 2023
Smooth, exact rotational symmetrization for deep learning on point clouds Sergey Pozdnyakov Michele Ceriotti 3DPC 35 25 0 30 May 2023
Task-Equivariant Graph Few-shot Learning Sungwon Kim Junseok Lee Namkyeong Lee Wonjoon Kim Seung-Deok Choi Chanyoung Park 19 6 0 30 May 2023
Geometric Algebra Transformer Johann Brehmer P. D. Haan S. Behrends Taco S. Cohen 39 26 0 28 May 2023
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance Mircea Petrache Shubhendu Trivedi 35 22 0 27 May 2023
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science D. P. Kovács Ilyes Batatia E. Arany Gábor Csányi AI4CE 26 83 0 23 May 2023
Clifford Group Equivariant Neural Networks David Ruhe Johannes Brandstetter Patrick Forré 26 35 0 18 May 2023
Towards Multi-Layered 3D Garments Animation Yidi Shao Chen Change Loy Bo Dai 3DH AI4CE 29 10 0 17 May 2023
An Exploration of Conditioning Methods in Graph Neural Networks Yeskendir Koishekenov Erik J. Bekkers AI4CE 37 3 0 03 May 2023
Geometric Latent Diffusion Models for 3D Molecule Generation Minkai Xu Alexander Powers R. Dror Stefano Ermon J. Leskovec DiffM AI4CE 55 135 0 02 May 2023
FAENet: Frame Averaging Equivariant GNN for Materials Modeling Alexandre Duval Victor Schmidt A. Garcia Santiago Miret Fragkiskos D. Malliaros Yoshua Bengio David Rolnick 31 54 0 28 Apr 2023
MUDiff: Unified Diffusion for Complete Molecule Generation Chenqing Hua Sitao Luan Minkai Xu Rex Ying Jie Fu Stefano Ermon Doina Precup DiffM 50 34 0 28 Apr 2023
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size Albert Musaelian A. Johansson Simon L. Batzner Boris Kozinsky 29 48 0 20 Apr 2023
Towards Controllable Diffusion Models via Reward-Guided Exploration Heng Zhang Tingyang Xu 21 2 0 14 Apr 2023
A Comprehensive Survey on Deep Graph Representation Learning Wei Ju Zheng Fang Yiyang Gu Zequn Liu Qingqing Long ... Jingyang Yuan Yusheng Zhao Yifan Wang Xiao Luo Ming Zhang GNN AI4TS 48 141 0 11 Apr 2023
A new perspective on building efficient and expressive 3D equivariant graph neural networks Weitao Du Yuanqi Du Limei Wang Dieqiao Feng Guifeng Wang Shuiwang Ji Carla P. Gomes Zhixin Ma AI4CE 29 33 0 07 Apr 2023
Learning Harmonic Molecular Representations on Riemannian Manifold Yiqun Wang Yuning Shen Shih‐Ya Chen Lihao Wang Fei Ye Hao Zhou 43 12 0 27 Mar 2023
The Exact Sample Complexity Gain from Invariances for Kernel Regression B. Tahmasebi Stefanie Jegelka 28 17 0 24 Mar 2023
Rethinking SO(3)-equivariance with Bilinear Tensor Networks C. Shimmin Zhelun Li Ema Smith 3DPC 20 0 0 20 Mar 2023
Ewald-based Long-Range Message Passing for Molecular Graphs Arthur Kosmala Johannes Gasteiger Nicholas Gao Stephan Günnemann 68 25 0 08 Mar 2023
A General Theory of Correct, Incorrect, and Extrinsic Equivariance Dian Wang Xu Zhu Jung Yeon Park Mingxi Jia Guanang Su Robert W. Platt Robin G. Walters 26 13 0 08 Mar 2023
Wigner kernels: body-ordered equivariant machine learning without a basis Filippo Bigi Sergey Pozdnyakov Michele Ceriotti 32 15 0 07 Mar 2023
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials Yuyang Wang Chang Xu Zijie Li A. Farimani AAML AI4CE 19 21 0 03 Mar 2023
Completeness of Atomic Structure Representations M. J. Willatt Sergey Pozdnyakov Christoph Ortner Michele Ceriotti 15 12 0 28 Feb 2023
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures Nhat-Khang Ngô Truong Son-Hy Risi Kondor ViT AI4CE 29 2 0 17 Feb 2023
Data efficiency and extrapolation trends in neural network interatomic potentials Joshua A Vita Daniel Schwalbe-Koda 34 16 0 12 Feb 2023
Is Distance Matrix Enough for Geometric Deep Learning? Zian Li Xiyuan Wang Yinan Huang Muhan Zhang 37 17 0 11 Feb 2023
Geometry-Complete Diffusion for 3D Molecule Generation and Optimization Alex Morehead Jianlin Cheng DiffM 14 22 0 08 Feb 2023
Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling Zheng Yuan Yaoyun Zhang Chuanqi Tan Wei Wang Feiran Huang Songfang Huang AI4CE ViT 24 6 0 02 Feb 2023
On the Expressive Power of Geometric Graph Neural Networks Chaitanya K. Joshi Cristian Bodnar Simon V. Mathis Taco Cohen Pietro Liò 52 83 0 23 Jan 2023
Spatial Attention Kinetic Networks with E(n)-Equivariance Yuanqing Wang J. Chodera 35 15 0 21 Jan 2023
SCARP: 3D Shape Completion in ARbitrary Poses for Improved Grasping Bipasha Sen Aditya Agarwal Gaurav Singh B. Brojeshwar Srinath Sridhar Madhava Krishna 3DPC 35 10 0 17 Jan 2023
INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation Ning Liu Yue Yu Huaiqian You Neeraj Tatikola AI4CE 18 23 0 29 Dec 2022
Lorentz group equivariant autoencoders Zichun Hao Raghav Kansal Javier Mauricio Duarte N. Chernyavskaya BDL DRL AI4CE 26 23 0 14 Dec 2022