Equivariant message passing for the prediction of tensorial properties and molecular spectra

5 February 2021

Oliver T. Unke

Papers citing "Equivariant message passing for the prediction of tensorial properties and molecular spectra"

50 / 81 papers shown

Title
Transferable Learning of Reaction Pathways from Geometric Priors Juno Nam Miguel Steiner Max Misterka Soojung Yang Avni P. Singhal Rafael Gómez-Bombarelli 32 0 0 21 Apr 2025
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems Alexander Windmann Henrik S. Steude Daniel Boschmann Oliver Niggemann OOD AI4TS 33 0 0 04 Apr 2025
Optimal Invariant Bases for Atomistic Machine Learning Alice Allen Emily Shinkle Roxana Bujack Nicholas Lubbers 37 0 0 30 Mar 2025
Equivariant Masked Position Prediction for Efficient Molecular Representation Junyi An C. Qu Yun-Fei Shi XinHao Liu Qianwei Tang Fenglei Cao Yuan Qi 35 0 0 12 Feb 2025
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials Matthias Holzenkamp Dongyu Lyu Ulrich Kleinekathöfer Peter Zaspel 33 0 0 10 Jan 2025
Symmetries-enhanced Multi-Agent Reinforcement Learning N. Bousias Stefanos Pertigkiozoglou Kostas Daniilidis George Pappas AI4CE 73 0 0 02 Jan 2025
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs Yuanchang Zhou Siyu Hu Chen Wang Lin-Wang Wang Guangming Tan Weile Jia AI4CE GNN 50 0 0 30 Dec 2024
Relaxed Equivariance via Multitask Learning Ahmed A. A. Elhag T. Konstantin Rusch Francesco Di Giovanni Michael Bronstein 42 2 0 23 Oct 2024
Learning Equivariant Non-Local Electron Density Functionals Nicholas Gao Eike Eberhard Stephan Günnemann 28 1 0 10 Oct 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials Makoto Takamoto Viktor Zaverkin Mathias Niepert AI4CE 57 0 0 23 Jul 2024
On the Expressive Power of Sparse Geometric MPNNs Yonatan Sverdlov Nadav Dym 40 1 0 02 Jul 2024
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
Evaluating representation learning on the protein structure universe Arian R. Jamasb Alex Morehead Chaitanya K. Joshi Zuobai Zhang Kieran Didi ... Charles Harris Jian Tang Jianlin Cheng Pietro Lio Tom L. Blundell SSL 38 12 0 19 Jun 2024
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
Equivariant Neural Tangent Kernels Philipp Misof Pan Kessel Jan E. Gerken 50 0 0 10 Jun 2024
A Recipe for Charge Density Prediction Xiang Fu Andrew S. Rosen Kyle Bystrom Rui Wang Albert Musaelian Boris Kozinsky Tess E. Smidt Tommi Jaakkola 48 5 0 29 May 2024
Guided Multi-objective Generative AI to Enhance Structure-based Drug Design Amit Kadan Kevin Ryczko Erika Lloyd A. Roitberg Takeshi Yamazaki 85 1 0 20 May 2024
Graph as Point Set Xiyuan Wang Pan Li Muhan Zhang GNN 3DPC PINN 40 4 0 05 May 2024
Grappa -- A Machine Learned Molecular Mechanics Force Field Leif Seute Eric Hartmann Jan Stühmer Frauke Gräter 29 3 0 25 Mar 2024
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications Jiaqi Han Jiacheng Cen Liming Wu Zongzhao Li Xiangzhe Kong ... Zhewei Wei Deli Zhao Yu Rong Wenbing Huang Wenbing Huang AI4CE 34 20 0 01 Mar 2024
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers Md Shamim Hussain Mohammed J. Zaki D. Subramanian ViT 26 5 0 07 Feb 2024
On the Completeness of Invariant Geometric Deep Learning Models Zian Li Xiyuan Wang Shijia Kang Muhan Zhang 31 2 0 07 Feb 2024
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water Viktor Zaverkin David Holzmüller Robin Schuldt Johannes Kastner 22 15 0 03 Dec 2023
Multiscale Hodge Scattering Networks for Data Analysis Naoki Saito Stefan C. Schonsheck Eugene Shvarts 32 1 0 17 Nov 2023
Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel Xuan Li Zhanke Zhou Jiangchao Yao Yu Rong Lu Zhang Bo Han 31 3 0 02 Nov 2023
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations Vaibhav Bihani Utkarsh Pratiush Sajid Mannan Tao Du Zhimin Chen Santiago Miret Matthieu Micoulaut M. Smedskjaer Sayan Ranu N. M. A. Krishnan 24 19 0 03 Oct 2023
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite Stefania Costantini Gianluca Galletti Fabian Fritz Stefan Adami Nikolaus A. Adams 40 13 0 28 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 30 16 0 15 Sep 2023
Beyond MD17: the reactive xxMD dataset Zihan Pengmei Junyu Liu Yinan Shu 21 6 0 22 Aug 2023
Fractional Denoising for 3D Molecular Pre-training Shi Feng Yuyan Ni Yanyan Lan Zhiming Ma Wei-Ying Ma DiffM AI4CE 42 25 0 20 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 13 20 0 15 Jun 2023
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning Shuxin Zheng Jiyan He Chang-Shu Liu Yu Shi Ziheng Lu ... Peiran Jin Chi Chen Frank Noé Haiguang Liu Tie-Yan Liu AI4CE 19 40 0 08 Jun 2023
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning Xiangzhe Kong Wen-bing Huang Yang Liu 20 13 0 02 Jun 2023
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics M. Schreiner Ole Winther Simon Olsson OOD AI4CE 41 13 0 29 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
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size Albert Musaelian A. Johansson Simon L. Batzner Boris Kozinsky 27 48 0 20 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 31 141 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 27 33 0 07 Apr 2023
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks Tim Rensmeyer Benjamin Craig D. Kramer Oliver Niggemann BDL 36 3 0 05 Apr 2023
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods Artur P. Toshev Ludger Paehler A. Panizza Nikolaus A. Adams AI4CE PINN 11 5 0 31 Mar 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
Geometric Deep Learning for Molecular Crystal Structure Prediction Michael Kilgour J. Rogal M. Tuckerman 13 16 0 17 Mar 2023
Connectivity Optimized Nested Graph Networks for Crystal Structures R. Ruff Patrick Reiser Jan Stuhmer Pascal Friederich GNN 26 10 0 27 Feb 2023
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability Zhiqiang Zhong A. Barkova Davide Mottin 22 8 0 16 Feb 2023
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs Saro Passaro C. L. Zitnick 3DPC 26 79 0 07 Feb 2023
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction Dominic Masters Josef Dean Kerstin Klaser Zhiyi Li Sam Maddrell-Mander ... D. Beker Andrew Fitzgibbon Shenyang Huang Ladislav Rampášek Dominique Beaini 28 8 0 06 Feb 2023
Implicit Convolutional Kernels for Steerable CNNs Maksim Zhdanov Nico Hoffmann Gabriele Cesa 26 5 0 12 Dec 2022
Capturing long-range interaction with reciprocal space neural network Hongyu Yu Liangliang Hong Shiyou Chen X. Gong Hongjun Xiang 27 11 0 30 Nov 2022
AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials Janice Lan Aini Palizhati Muhammed Shuaibi Brandon M. Wood Brook Wander Abhishek Das M. Uyttendaele C. L. Zitnick Zachary W. Ulissi 21 44 0 29 Nov 2022
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction Yuancheng Sun Yimeng Chen Weizhi Ma Wenhao Huang Kang Liu Zhiming Ma Wei-Ying Ma Yanyan Lan 18 7 0 18 Oct 2022