Directional Message Passing for Molecular Graphs

6 March 2020

Stephan Günnemann

Papers citing "Directional Message Passing for Molecular Graphs"

50 / 450 papers shown

Title
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study Sadman Sadeed Omee Nihang Fu Rongzhi Dong Ming Hu Jianjun Hu OOD 31 17 0 16 Jan 2024
On the Expressive Power of Graph Neural Networks Ashwin Nalwade Kelly Marshall Axel Eladi Umang Sharma 19 0 0 03 Jan 2024
Molecular Hypergraph Neural Networks Junwu Chen Philippe Schwaller GNN 44 10 0 20 Dec 2023
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials Kyle Noordhoek Christopher J. Bartel AI4CE 22 6 0 18 Dec 2023
Non-Euclidean Spatial Graph Neural Network Zhengwu Zhang Sirui Li Jingcheng Zhou Junxiang Wang Abhinav Angirekula Allen Zhang Liang Zhao GNN 19 0 0 17 Dec 2023
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction Andac Demir Francis Prael B. Kiziltan 19 2 0 12 Dec 2023
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks Haoyu Li Shichang Zhang Longwen Tang Mathieu Bauchy Yizhou Sun AI4CE 45 1 0 08 Dec 2023
PerCNet: Periodic Complete Representation for Crystal Graphs Jiao Huang Qianli Xing Jinglong Ji Bo Yang 34 1 0 03 Dec 2023
GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks Zizhang Chen R. P. Badman Lachele Foley Robert Woods Pengyu Hong 38 0 0 28 Nov 2023
A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems Shuo-feng Zhang Yang Liu Lei Xie AI4CE GNN PINN 31 10 0 19 Nov 2023
Equivariant Neural Operator Learning with Graphon Convolution Chaoran Cheng Jian-wei Peng 16 4 0 17 Nov 2023
Multiparameter Persistent Homology for Molecular Property Prediction Andac Demir B. Kiziltan 22 1 0 17 Nov 2023
Gradual Optimization Learning for Conformational Energy Minimization Artem Tsypin L. Ugadiarov Kuzma Khrabrov Alexander Telepov Egor Rumiantsev Alexey Skrynnik Aleksandr I. Panov Dmitry Vetrov E. Tutubalina Artur Kadurin 24 1 0 05 Nov 2023
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method Yuyan Ni Shikun Feng Wei-Ying Ma Zhiming Ma Yanyan Lan DiffM AI4CE 29 10 0 03 Nov 2023
Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations D. Rothchild Andrew S. Rosen Eric Taw Connie Robinson Joseph E. Gonzalez Aditi S. Krishnapriyan DiffM 26 2 0 02 Nov 2023
$Generating QM1B with PySCF$_{\text{IPU}}$$ Generating QM1B with PySCF $_{\text{IPU}}$ Alexander Mathiasen Hatem Helal Kerstin Klaser Paul Balanca Josef Dean Carlo Luschi Dominique Beaini Andrew Fitzgibbon Dominic Masters 25 1 0 02 Nov 2023
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture Anuroop Sriram Sihoon Choi Xiaohan Yu Logan M. Brabson Abhishek Das Zachary W. Ulissi Matthew Uyttendaele A. Medford D. Sholl AI4CE 27 35 0 01 Nov 2023
Role of Structural and Conformational Diversity for Machine Learning Potentials Nikhil Shenoy Prudencio Tossou Emmanuel Noutahi Hadrien Mary Dominique Beaini Jiarui Ding AI4CE 17 0 0 30 Oct 2023
Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes Cai Zhou Xiyuan Wang Muhan Zhang 36 14 0 30 Oct 2023
Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach Phillip Pope David Jacobs 24 3 0 28 Oct 2023
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction Nima Shoghi Adeesh Kolluru John R. Kitchin Zachary W. Ulissi C. L. Zitnick Brandon M. Wood AI4CE 24 32 0 25 Oct 2023
Improving Molecular Properties Prediction Through Latent Space Fusion Eduardo Soares Akihiro Kishimoto E. V. Brazil Seiji Takeda Hiroshi Kajino Renato F. G. Cerqueira BDL AI4CE 18 2 0 20 Oct 2023
Learning Interatomic Potentials at Multiple Scales Xiang Fu Albert Musaelian Anders Johansson Tommi Jaakkola Boris Kozinsky 29 2 0 20 Oct 2023
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design Xiang Fu Tian Xie Andrew S. Rosen Tommi Jaakkola Jake A. Smith DiffM 36 9 0 16 Oct 2023
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning A. Maharana Prateek Yadav Mohit Bansal 24 28 0 11 Oct 2023
On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space Zihan Zhou Ruiying Liu Tianshu Yu DiffM 30 0 0 07 Oct 2023
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets Dominique Beaini Shenyang Huang Joao Alex Cunha Zhiyi Li Gabriela Moisescu-Pareja ... Thérence Bois Andrew Fitzgibbon Bla.zej Banaszewski Chad Martin Dominic Masters AI4CE 28 19 0 06 Oct 2023
Fragment-based Pretraining and Finetuning on Molecular Graphs Kha-Dinh Luong Ambuj Singh AI4CE 25 12 0 05 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
Probabilistically Rewired Message-Passing Neural Networks Chendi Qian Andrei Manolache Kareem Ahmed Zhe Zeng Guy Van den Broeck Mathias Niepert Christopher Morris 39 12 0 03 Oct 2023
On Training Derivative-Constrained Neural Networks KaiChieh Lo Daniel Huang 26 3 0 02 Oct 2023
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks Yanqiao Zhu Jeehyun Hwang Keir Adams Zhen Liu B. Nan ... Olaf Wiest Olexandr Isayev Connor W. Coley Yizhou Sun Wei Wang 19 6 0 29 Sep 2023
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information Taojie Kuang Yiming Ren Zhixiang Ren 25 7 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
Uncovering Neural Scaling Laws in Molecular Representation Learning Dingshuo Chen Yanqiao Zhu Jieyu Zhang Yuanqi Du Zhixun Li Qiang Liu Shu Wu Liang Wang 32 16 0 15 Sep 2023
Molecular Conformation Generation via Shifting Scores Zihan Zhou Ruiying Liu Chaolong Ying Ruimao Zhang Tianshu Yu DiffM 29 2 0 12 Sep 2023
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling Kin Long Kelvin Lee Carmelo Gonzales Marcel Nassar Matthew Spellings Mikhail Galkin Santiago Miret 32 15 0 12 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
PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer Rui Feng Huan Tran Aubrey Toland Binghong Chen Qi Zhu R. Ramprasad Chao Zhang 11 1 0 01 Sep 2023
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations Rui Feng Qi Zhu Huan Tran Binghong Chen Aubrey Toland R. Ramprasad Chao Zhang AI4CE 28 9 0 24 Aug 2023
Dual-Balancing for Multi-Task Learning Baijiong Lin Weisen Jiang Feiyang Ye Yu Zhang Pengguang Chen Yingke Chen Shu Liu James T. Kwok CVBM 28 12 0 23 Aug 2023
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness Francesco Campi Lukas Gosch Thomas Wollschläger Yan Scholten Stephan Günnemann AAML 56 2 0 16 Aug 2023
Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling T. Pakornchote Natthaphon Choomphon-anomakhun Sorrjit Arrerut C. Atthapak S. Khamkaeo Thiparat Chotibut T. Bovornratanaraks DiffM 29 17 0 04 Aug 2023
Factor Graph Neural Networks Zhen Zhang Mohammed Haroon Dupty Fan Wu Javen Qinfeng Shi Fan Wu AI4CE 30 40 0 02 Aug 2023
Improvable Gap Balancing for Multi-Task Learning Yanqi Dai Nanyi Fei Zhiwu Lu 40 4 0 28 Jul 2023
Learning Universal and Robust 3D Molecular Representations with Graph Convolutional Networks Shuo-feng Zhang Yang Liu Li Xie Lei Xie 3DV 18 0 0 24 Jul 2023
Fractional Denoising for 3D Molecular Pre-training Shi Feng Yuyan Ni Yanyan Lan Zhiming Ma Wei-Ying Ma DiffM AI4CE 47 25 0 20 Jul 2023
ChiENN: Embracing Molecular Chirality with Graph Neural Networks Piotr Gaiñski Michał Koziarski Jacek Tabor Marek Śmieja GNN 34 3 0 05 Jul 2023
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction? Jun-Xiong Xia Lecheng Zhang Xiao Zhu Stan Z. Li 26 3 0 30 Jun 2023
Accelerating Molecular Graph Neural Networks via Knowledge Distillation Filip Ekstrom Kelvinius D. Georgiev Artur P. Toshev Johannes Gasteiger 26 7 0 26 Jun 2023