mlsanityData to 7 Oct 2026

E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

arXiv 2101.03164 ↗First posted 8 Jan 2021physics.comp-ph cond-mat.mtrl-sci cs.LGcited by 701

Not on the map. Why?

Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

Key facts. Cited by 701 papers in the map (data through 7 Oct 2026). Builds on 17 of its 67 references that are in the map. First posted on arXiv 8 Jan 2021.

Builds on

17 of 67 references.

References · 17

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  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  5. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  7. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
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  9. 2017Deep Learning Scaling is Predictable, Empiricallycited by 556
  10. 2020Directional Message Passing for Molecular Graphscited by 556
  11. 20183D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Datacited by 348
  12. 2019Cormorant: Covariant Molecular Neural Networkscited by 262
5 more
  1. 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 213
  2. 2018Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Networkcited by 168
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 92; 2023: 125; 2024: 141; 2025: 169; 2026: 156.

701 papers.

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  1. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  2. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
  3. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  4. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  5. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  6. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  7. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 229
  8. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 213
  9. 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
  10. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 185
  11. 2022Graph neural networks for materials science and chemistrycited by 181
  12. 2022e3nn: Euclidean Neural Networkscited by 171
38 more
  1. 2021Spherical Message Passing for 3D Graph Networkscited by 166
  2. 2022EquiBind: Geometric Deep Learning for Drug Binding Structure Predictioncited by 143
  3. 2022Pre-training via Denoising for Molecular Property Predictioncited by 124
  4. 2021Geometric Deep Learning on Molecular Representationscited by 113
  5. 2023Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNscited by 113
  6. 2022Broken Neural Scaling Lawscited by 102
  7. 2022The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentialscited by 100
  8. 2022The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalystscited by 95
  9. 2021Inverse design of 3d molecular structures with conditional generative neural networkscited by 93
  10. 2022Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulationscited by 92
  11. 2023On the Expressive Power of Geometric Graph Neural Networkscited by 89
  12. 2022Periodic Graph Transformers for Crystal Material Property Predictioncited by 87
  13. 202314 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathoncited by 85
  14. 2022SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentialscited by 80
  15. 2025Learning Smooth and Expressive Interatomic Potentials for Physical Property Predictioncited by 79
  16. 2023FAENet: Frame Averaging Equivariant GNN for Materials Modelingcited by 72
  17. 2022ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphscited by 69
  18. 2021SE(3)-equivariant prediction of molecular wavefunctions and electronic densitiescited by 67
  19. 2024Orb: A Fast, Scalable Neural Network Potentialcited by 63
  20. 2022GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasetscited by 61
  21. 2022Geometrically Equivariant Graph Neural Networks: A Surveycited by 55
  22. 2023Swallowing the Bitter Pill: Simplified Scalable Conformer Generationcited by 55
  23. 2023Clifford Group Equivariant Neural Networkscited by 54
  24. 2023How does over-squashing affect the power of GNNs?cited by 53
  25. 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentialscited by 53
  26. 2023Scalable Diffusion for Materials Generationcited by 52
  27. 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 52
  28. 2022The Lie Derivative for Measuring Learned Equivariancecited by 51
  29. 2023Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulationcited by 51
  30. 2023Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generationcited by 50
  31. 2024Open Materials 2024 (OMat24) Inorganic Materials Dataset and Modelscited by 50
  32. 2024Does equivariance matter at scale?cited by 49
  33. 2024KAGNNs: Kolmogorov-Arnold Networks meet Graph Learningcited by 48
  34. 2022Spherical Channels for Modeling Atomic Interactionscited by 47
  35. 2023Geometric Algebra Transformercited by 46
  36. 2022A Self-Attention Ansatz for Ab-initio Quantum Chemistrycited by 45
  37. 2021Simple GNN Regularisation for 3D Molecular Property Prediction & Beyondcited by 44
  38. 2023OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentialscited by 44

50 most cited of 701 All 701 (JSON)