{"total":73,"rows":[{"id":"2312.07511","title":"A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems","created":"2023-12-12","cited_by_count":68,"area":null},{"id":"2402.16077","title":"Equivariant Frames and the Impossibility of Continuous Canonicalization","created":"2024-02-25","cited_by_count":43,"area":null},{"id":"2305.19302","title":"Smooth, exact rotational symmetrization for deep learning on point clouds","created":"2023-05-30","cited_by_count":39,"area":null},{"id":"2310.04925","title":"Crystal-GFN: sampling crystals with desirable properties and constraints","created":"2023-10-07","cited_by_count":31,"area":null},{"id":"2403.00485","title":"A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications","created":"2024-03-01","cited_by_count":28,"area":null},{"id":"2405.18378","title":"A Canonicalization Perspective on Invariant and Equivariant Learning","created":"2024-05-28","cited_by_count":27,"area":null},{"id":"2403.17381","title":"Application-Driven Innovation in Machine Learning","created":"2024-03-26","cited_by_count":24,"area":null},{"id":"2406.07598","title":"Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency","created":"2024-06-11","cited_by_count":19,"area":null},{"id":"2403.09549","title":"Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields","created":"2024-03-14","cited_by_count":18,"area":null},{"id":"2410.11443","title":"Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?","created":"2024-10-15","cited_by_count":18,"area":null},{"id":"2403.11686","title":"Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding","created":"2024-03-18","cited_by_count":15,"area":null},{"id":"2505.10994","title":"Space Group Equivariant Crystal Diffusion","created":"2025-05-16","cited_by_count":15,"area":null},{"id":"2309.05934","title":"MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling","created":"2023-09-12","cited_by_count":14,"area":null},{"id":"2406.17747","title":"Probing the effects of broken symmetries in machine learning","created":"2024-06-25","cited_by_count":13,"area":null},{"id":"2410.17878","title":"Relaxed Equivariance via Multitask Learning","created":"2024-10-23","cited_by_count":13,"area":null},{"id":"2403.19800","title":"Gegenbauer Graph Neural Networks for Time-varying Signal Reconstruction","created":"2024-03-28","cited_by_count":12,"area":null},{"id":"2305.09900","title":"Efficient Equivariant Transfer Learning from Pretrained Models","created":"2023-05-17","cited_by_count":11,"area":null},{"id":"2402.04836","title":"On the Completeness of Invariant Geometric Deep Learning Models","created":"2024-02-07","cited_by_count":11,"area":null},{"id":"2406.00183","title":"Predicting solvation free energies with an implicit solvent machine learning potential","created":"2024-05-31","cited_by_count":10,"area":null},{"id":"2503.08305","title":"ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals","created":"2025-03-11","cited_by_count":10,"area":null},{"id":"2410.11933","title":"Beyond Sequence: Impact of Geometric Context for RNA Property Prediction","created":"2024-10-15","cited_by_count":8,"area":null},{"id":"2405.14089","title":"Improved Canonicalization for Model Agnostic Equivariance","created":"2024-05-23","cited_by_count":7,"area":null},{"id":"2410.15728","title":"Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases","created":"2024-10-21","cited_by_count":5,"area":null},{"id":"2412.12540","title":"Stiefel Flow Matching for Moment-Constrained Structure Elucidation","created":"2024-12-17","cited_by_count":5,"area":"diffusion"},{"id":"2510.00027","title":"Learning Inter-Atomic Potentials without Explicit Equivariance","created":"2025-09-25","cited_by_count":5,"area":null},{"id":"2409.01931","title":"On the design space between molecular mechanics and machine learning force fields","created":"2024-09-03","cited_by_count":4,"area":null},{"id":"2506.13051","title":"Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning","created":"2025-06-16","cited_by_count":4,"area":null},{"id":"2508.19071","title":"Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks","created":"2025-08-26","cited_by_count":4,"area":null},{"id":"2508.20875","title":"LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modeling","created":"2025-08-28","cited_by_count":4,"area":null},{"id":"2510.09768","title":"Scaling Laws and Symmetry, Evidence from Neural Force Fields","created":"2025-10-10","cited_by_count":4,"area":null},{"id":"2402.19369","title":"Structure Preserving Diffusion Models","created":"2024-02-29","cited_by_count":3,"area":"diffusion"},{"id":"2509.01170","title":"ADMP-GNN: Adaptive Depth Message Passing GNN","created":"2025-09-01","cited_by_count":3,"area":null},{"id":"2509.02196","title":"Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space","created":"2025-09-02","cited_by_count":3,"area":null},{"id":"2509.24886","title":"Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks","created":"2025-09-29","cited_by_count":3,"area":null},{"id":"2510.02142","title":"Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study","created":"2025-10-02","cited_by_count":3,"area":null},{"id":"2510.13169","title":"Universally Invariant Learning in Equivariant GNNs","created":"2025-10-15","cited_by_count":3,"area":null},{"id":"2512.10723","title":"Generalized Spherical Neural Operators: Green's Function Formulation","created":"2025-12-11","cited_by_count":3,"area":null},{"id":"2310.11609","title":"Reflection-Equivariant Diffusion for 3D Structure Determination from Isotopologue Rotational Spectra in Natural Abundance","created":"2023-10-17","cited_by_count":2,"area":null},{"id":"2401.08627","title":"Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks","created":"2023-12-08","cited_by_count":2,"area":null},{"id":"2411.08638","title":"Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation","created":"2024-11-13","cited_by_count":2,"area":null},{"id":"2501.07155","title":"AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential","created":"2025-01-13","cited_by_count":2,"area":null},{"id":"2502.13522","title":"Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements","created":"2025-02-19","cited_by_count":2,"area":null},{"id":"2503.12919","title":"Continuous Simplicial Neural Networks","created":"2025-03-17","cited_by_count":2,"area":null},{"id":"2506.16189","title":"CP$^2$: Leveraging Geometry for Conformal Prediction via Canonicalization","created":"2025-06-19","cited_by_count":2,"area":null},{"id":"2605.08262","title":"SLayerGen: a Crystal Generative Model for all Space and Layer Groups","created":"2026-05-07","cited_by_count":2,"area":null},{"id":"2310.06682","title":"On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions","created":"2023-10-10","cited_by_count":1,"area":null},{"id":"2310.09675","title":"Efficient Model-Agnostic Multi-Group Equivariant Networks","created":"2023-10-14","cited_by_count":1,"area":null},{"id":"2411.04570","title":"Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks","created":"2024-11-07","cited_by_count":1,"area":null},{"id":"2502.05318","title":"Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schr\\\"odinger Equation","created":"2025-02-07","cited_by_count":1,"area":null},{"id":"2505.04808","title":"Piecewise Constant Spectral Graph Neural Network","created":"2025-05-07","cited_by_count":1,"area":null},{"id":"2505.12137","title":"Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding","created":"2025-05-17","cited_by_count":1,"area":null},{"id":"2506.00302","title":"Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework","created":"2025-05-30","cited_by_count":1,"area":null},{"id":"2507.00407","title":"Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials","created":"2025-07-01","cited_by_count":1,"area":null},{"id":"2507.01131","title":"Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations","created":"2025-07-01","cited_by_count":1,"area":null},{"id":"2602.20370","title":"Quantitative Approximation Rates for Group Equivariant Learning","created":"2026-02-23","cited_by_count":1,"area":null},{"id":"2603.24638","title":"How unconstrained machine-learning models learn physical symmetries","created":"2026-03-25","cited_by_count":1,"area":null},{"id":"2605.20581","title":"TriForces: Augmenting Atomistic GNNs for Transferable Representations","created":"2026-05-20","cited_by_count":1,"area":null},{"id":"2606.19784","title":"EquiVLA: A General Framework for Rotationally Equivariant Vision-Language-Action Models","created":"2026-06-18","cited_by_count":1,"area":"vla"},{"id":"2606.29584","title":"Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials","created":"2026-06-28","cited_by_count":1,"area":null},{"id":"2403.01671","title":"Approximating invariant functions with the sorting trick is theoretically justified","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2406.00347","title":"E$^3$-Net: Efficient E(3)-Equivariant Normal Estimation Network","created":"2024-06-01","cited_by_count":0,"area":null},{"id":"2407.08313","title":"Improving Molecular Modeling with Geometric GNNs: an Empirical Study","created":"2024-07-11","cited_by_count":0,"area":null},{"id":"2410.06262","title":"SymDiff: Equivariant Diffusion via Stochastic Symmetrisation","created":"2024-10-08","cited_by_count":0,"area":"diffusion"},{"id":"2503.02209","title":"CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling","created":"2025-03-04","cited_by_count":0,"area":null},{"id":"2505.09174","title":"Quotient Complex Transformer (QCformer) for Perovskite Data Analysis","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2510.05494","title":"Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2512.01519","title":"QuantumCanvas: A Multimodal Benchmark for Visual Learning of Atomic Interactions","created":"2025-12-01","cited_by_count":0,"area":null},{"id":"2601.03561","title":"Neural Operators for Biomedical Spherical Heterogeneity","created":"2026-01-07","cited_by_count":0,"area":null},{"id":"2602.07087","title":"Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2605.08286","title":"Diagnosing Spectral Ceilings in Equivariant Neural Force Fields","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2608.12010","title":"Reducing Symmetry Increase in Equivariant Neural Networks","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2608.28853","title":"Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs","created":"2026-08-28","cited_by_count":0,"area":null},{"id":"2610.15171","title":"LexiCan: Lexicographic Canonicalization of Point Clouds for Machine Learning Applications","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}