Data to 7 Oct 2026

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

arXiv 2011.14115 ↗First posted 28 Nov 2020cs.LG physics.chem-ph physics.comp-phcited by 254

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Abstract (arXiv)

Many important tasks in chemistry revolve around molecules during reactions. This requires predictions far from the equilibrium, while most recent work in machine learning for molecules has been focused on equilibrium or near-equilibrium states. In this paper we aim to extend this scope in three ways. First, we propose the DimeNet++ model, which is 8x faster and 10% more accurate than the original DimeNet on the QM9 benchmark of equilibrium molecules. Second, we validate DimeNet++ on highly reactive molecules by developing the challenging COLL dataset, which contains distorted configurations of small molecules during collisions. Finally, we investigate ensembling and mean-variance estimation for uncertainty quantification with the goal of accelerating the exploration of the vast space of non-equilibrium structures. Our DimeNet++ implementation as well as the COLL dataset are available online.

Key facts. Cited by 254 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Nov 2020.

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Citing papers per year: 2021: 27; 2022: 47; 2023: 61; 2024: 39; 2025: 42; 2026: 38.

254 papers.

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2021E(n) Equivariant Graph Neural Networkscited by 951
2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 268
2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
2021Geometric and Physical Quantities Improve E(3) Equivariant Message Passingcited by 225
2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
2022Graph neural networks for materials science and chemistrycited by 181
2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179

38 more

2021Spherical Message Passing for 3D Graph Networkscited by 166
20213D Infomax improves GNNs for Molecular Property Predictioncited by 146
2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 142
2022Pre-training via Denoising for Molecular Property Predictioncited by 124
2021Geometric Deep Learning on Molecular Representationscited by 113
2023Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNscited by 113
2022The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalystscited by 95
2022Periodic Graph Transformers for Crystal Material Property Predictioncited by 87
2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 72
2023FAENet: Frame Averaging Equivariant GNN for Materials Modelingcited by 72
2022ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphscited by 69
2022Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matchingcited by 67
2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
2022GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasetscited by 61
2021ForceNet: A Graph Neural Network for Large-Scale Quantum Calculationscited by 58
2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentialscited by 53
2021Rotation Invariant Graph Neural Networks using Spin Convolutionscited by 52
2025SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Modelscited by 48
2022Spherical Channels for Modeling Atomic Interactionscited by 47
2023Ewald-based Long-Range Message Passing for Molecular Graphscited by 46
2021Simple GNN Regularisation for 3D Molecular Property Prediction & Beyondcited by 44
2021Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphscited by 44
2023Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Spacecited by 40
2024The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domainscited by 40
2023Smooth, exact rotational symmetrization for deep learning on point cloudscited by 39
2023Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltoniancited by 38
2023Efficient Approximations of Complete Interatomic Potentials for Crystal Property Predictioncited by 37
2023Continual Learning on Dynamic Graphs via Parameter Isolationcited by 35
2024Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Productscited by 35
2022Crystal Twins: Self-supervised Learning for Crystalline Material Property Predictioncited by 34
2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33
2023Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic sizecited by 33
2023Fractional Denoising for 3D Molecular Pre-trainingcited by 33
2021Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotationscited by 29
2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
2021Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistrycited by 27
2021Learning neural network potentials from experimental data via Differentiable Trajectory Reweightingcited by 26

50 most cited of 254 All 254 (JSON)