Directional Message Passing for Molecular Graphs

6 March 2020

Stephan Günnemann

Papers citing "Directional Message Passing for Molecular Graphs"

50 / 450 papers shown

Title
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations Yidong Liao Brandon M. Wood Abhishek Das Tess E. Smidt 24 131 0 21 Jun 2023
Uncertainty Estimation for Molecules: Desiderata and Methods Tom Wollschlager Nicholas Gao Bertrand Charpentier Mohamed Amine Ketata Stephan Günnemann 21 9 0 20 Jun 2023
A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design Zaixin Zhang Jiaxian Yan Qi Liu Enhong Chen Marinka Zitnik 32 1 0 20 Jun 2023
P-tensors: a General Formalism for Constructing Higher Order Message Passing Networks Tianyi Sun Andrew R. Hands Risi Kondor 13 2 0 19 Jun 2023
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules Haiyang Yu Meng Liu Youzhi Luo A. Strasser X. Qian Xiaoning Qian Shuiwang Ji 13 20 0 15 Jun 2023
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials Shengchao Liu Weitao Du Yanjing Li Zhuoxinran Li Zhiling Zheng ... Anima Anandkumar C. Borgs J. Chayes Hongyu Guo Jian Tang AI4CE 31 19 0 15 Jun 2023
Automated 3D Pre-Training for Molecular Property Prediction Xu Wang Huan Zhao Weiwei Tu Quanming Yao AI4CE 26 35 0 13 Jun 2023
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction Yu-Ching Lin Keqiang Yan Youzhi Luo Yi Liu Xiaoning Qian Shuiwang Ji 66 33 0 12 Jun 2023
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials Guillem Simeon Gianni de Fabritiis 24 45 0 10 Jun 2023
Scaling Spherical CNNs Carlos Esteves Jean-Jacques E. Slotine A. Makadia GNN LRM 19 13 0 08 Jun 2023
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction Haomin Yu Yanru Song Jilin Hu Chenjuan Guo B. Yang AI4CE 19 4 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
Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction Jiaxian Yan Zhaofeng Ye Ziyi Yang Chengqiang Lu Shengyu Zhang Qi Liu J. Qiu 35 11 0 08 Jun 2023
XInsight: Revealing Model Insights for GNNs with Flow-based Explanations Eli J. Laird Ayesh Madushanka E. Kraka Corey Clark 17 1 0 07 Jun 2023
Optimized Crystallographic Graph Generation for Material Science Astrid Klipfel Y. Frégier A. Sayede Zied Bouraoui 12 1 0 07 Jun 2023
Unified Model for Crystalline Material Generation Astrid Klipfel Y. Frégier A. Sayede Zied Bouraoui 11 6 0 07 Jun 2023
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings Johannes Falk L. Bonati P. Novelli Michele Parinello Massimiliano Pontil 27 4 0 02 Jun 2023
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning Xiangzhe Kong Wen-bing Huang Yang Liu 20 13 0 02 Jun 2023
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing Yang Zhang Zuobai Zhang Bozitao Zhong Sanchit Misra Jian Tang DiffM 16 32 0 01 Jun 2023
Smooth, exact rotational symmetrization for deep learning on point clouds Sergey Pozdnyakov Michele Ceriotti 3DPC 35 25 0 30 May 2023
SO(2)-Equivariant Downwash Models for Close Proximity Flight Henry Smith Ajay Shankar Jennifer Gielis J. Blumenkamp A. Prorok 27 7 0 30 May 2023
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance Mircea Petrache Shubhendu Trivedi 33 22 0 27 May 2023
A Score-Based Model for Learning Neural Wavefunctions Xuan Zhang Shenglong Xu Shuiwang Ji DiffM 23 1 0 25 May 2023
Learning Lagrangian Fluid Mechanics with E( $3$ )-Equivariant Graph Neural Networks Artur P. Toshev Gianluca Galletti Johannes Brandstetter Stefan Adami Nikolaus A. Adams AI4CE 27 5 0 24 May 2023
Continual Learning on Dynamic Graphs via Parameter Isolation Peiyan Zhang Yuchen Yan Chaozhuo Li Senzhang Wang Xing Xie Guojie Song Sunghun Kim 58 35 0 23 May 2023
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning Jiahao Chen Yurou Liu Jiangmeng Li Bing-Huang Su Jirong Wen 21 0 0 22 May 2023
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation Han Huang Leilei Sun Bowen Du Weifeng Lv DiffM 24 16 0 21 May 2023
PANNA 2.0: Efficient neural network interatomic potentials and new architectures Franco Pellegrini Ruggero Lot Yusuf Shaidu E. Küçükbenli 15 9 0 19 May 2023
Clifford Group Equivariant Neural Networks David Ruhe Johannes Brandstetter Patrick Forré 26 34 0 18 May 2023
CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning Colin A. Grambow Hayley Weir Christian N Cunningham Tommaso Biancalani Kangway V Chuang 18 4 0 14 May 2023
E(n) Equivariant Message Passing Simplicial Networks Floor Eijkelboom Rob D. Hesselink Erik J. Bekkers 20 14 0 11 May 2023
Message Passing Neural Networks for Traffic Forecasting Arian Prabowo Hao Xue Wei Shao Piotr Koniusz Flora D. Salim GNN 23 6 0 09 May 2023
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks Cai Zhou Xiyuan Wang Muhan Zhang 26 14 0 08 May 2023
An Exploration of Conditioning Methods in Graph Neural Networks Yeskendir Koishekenov Erik J. Bekkers AI4CE 35 3 0 03 May 2023
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles Aik Rui Tan S. Urata Samuel Goldman Johannes C. B. Dietschreit Rafael Gómez-Bombarelli BDL 36 41 0 02 May 2023
Stress and heat flux via automatic differentiation Marcel F. Langer J. Frank Florian Knoop 22 9 0 02 May 2023
3D Molecular Geometry Analysis with 2D Graphs Zhao Xu Yaochen Xie Youzhi Luo Xuan Zhang Xinyi Xu Meng Liu Kaleb Dickerson Cheng Deng Maho Nakata Shuiwang Ji 19 1 0 01 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 29 53 0 28 Apr 2023
SELFormer: Molecular Representation Learning via SELFIES Language Models Atakan Yüksel Erva Ulusoy Atabey Ünlü Tunca Dogan 25 54 0 10 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 27 33 0 07 Apr 2023
Equivariant Parameter Sharing for Porous Crystalline Materials Marko Petković Pablo Romero-Marimon Vlado Menkovski Sofía Calero 26 1 0 04 Apr 2023
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising Hyeonsu Kim Jeheon Woo Seonghwan Kim Seokhyun Moon Jun Hyeong Kim Woo Youn Kim AI4CE 28 6 0 28 Mar 2023
Learning Harmonic Molecular Representations on Riemannian Manifold Yiqun Wang Yuning Shen Shih‐Ya Chen Lihao Wang Fei Ye Hao Zhou 35 12 0 27 Mar 2023
Geometric Deep Learning for Molecular Crystal Structure Prediction Michael Kilgour J. Rogal M. Tuckerman 13 16 0 17 Mar 2023
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+ Shuqi Lu Zhifeng Gao Di He Linfeng Zhang Guolin Ke 32 24 0 16 Mar 2023
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction Bo-Lan Wang Chen Liang Jiaze Wang Furui Liu Shaogang Hao Dong Li Jianye Hao Guangyong Chen Xiaolong Zou Pheng-Ann Heng 39 3 0 06 Mar 2023
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials Yuyang Wang Chang Xu Zijie Li A. Farimani AAML AI4CE 19 20 0 03 Mar 2023
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling B. Deng Peichen Zhong KyuJung Jun Janosh Riebesell K. Han Christopher J. Bartel Gerbrand Ceder 20 24 0 28 Feb 2023
Connectivity Optimized Nested Graph Networks for Crystal Structures R. Ruff Patrick Reiser Jan Stuhmer Pascal Friederich GNN 26 10 0 27 Feb 2023
Learning Topology-Specific Experts for Molecular Property Prediction S. Kim Dongha Lee SeongKu Kang Seonghyeon Lee Hwanjo Yu AAML 22 11 0 27 Feb 2023