{"total":69,"rows":[{"id":"2301.09308","title":"On the Expressive Power of Geometric Graph Neural Networks","created":"2023-01-23","cited_by_count":90,"area":null},{"id":"2209.11807","title":"Periodic Graph Transformers for Crystal Material Property Prediction","created":"2022-09-23","cited_by_count":87,"area":null},{"id":"2210.01765","title":"One Transformer Can Understand Both 2D & 3D Molecular Data","created":"2022-10-04","cited_by_count":77,"area":null},{"id":"2305.05577","title":"FAENet: Frame Averaging Equivariant GNN for Materials Modeling","created":"2023-04-28","cited_by_count":73,"area":null},{"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":"2207.12600","title":"Learning Hierarchical Protein Representations via Complete 3D Graph Networks","created":"2022-07-26","cited_by_count":55,"area":null},{"id":"2207.04869","title":"Graph-based Molecular Representation Learning","created":"2022-07-08","cited_by_count":51,"area":null},{"id":"2304.04757","title":"A new perspective on building efficient and expressive 3D equivariant graph neural networks","created":"2023-04-07","cited_by_count":47,"area":null},{"id":"2306.04922","title":"Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian","created":"2023-06-08","cited_by_count":39,"area":null},{"id":"2306.10045","title":"Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction","created":"2023-06-12","cited_by_count":37,"area":null},{"id":"2403.11857","title":"Complete and Efficient Graph Transformers for Crystal Material Property Prediction","created":"2024-03-18","cited_by_count":34,"area":null},{"id":"2305.04120","title":"A Latent Diffusion Model for Protein Structure Generation","created":"2023-05-06","cited_by_count":28,"area":"diffusion"},{"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":"2306.09549","title":"QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules","created":"2023-06-15","cited_by_count":26,"area":null},{"id":"2310.10732","title":"MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design","created":"2023-10-16","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":"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":"2211.02504","title":"Geometry-Complete Perceptron Networks for 3D Molecular Graphs","created":"2022-11-04","cited_by_count":16,"area":null},{"id":"2208.00716","title":"Graph Neural Network with Local Frame for Molecular Potential Energy Surface","created":"2022-08-01","cited_by_count":14,"area":null},{"id":"2310.11802","title":"De novo protein design using geometric vector field networks","created":"2023-10-18","cited_by_count":14,"area":null},{"id":"2502.19227","title":"Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems","created":"2025-02-26","cited_by_count":14,"area":null},{"id":"2501.19216","title":"E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products","created":"2025-01-31","cited_by_count":12,"area":null},{"id":"2503.00152","title":"Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation","created":"2025-02-28","cited_by_count":12,"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":"2301.13821","title":"Complete Neural Networks for Complete Euclidean Graphs","created":"2023-01-31","cited_by_count":10,"area":null},{"id":"2408.10120","title":"Geometry Informed Tokenization of Molecules for Language Model Generation","created":"2024-08-19","cited_by_count":10,"area":null},{"id":"2411.09429","title":"AI-driven inverse design of materials: Past, present and future","created":"2024-11-14","cited_by_count":10,"area":null},{"id":"2407.02025","title":"On the Expressive Power of Sparse Geometric MPNNs","created":"2024-07-02","cited_by_count":7,"area":null},{"id":"2506.20743","title":"A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools","created":"2025-06-25","cited_by_count":7,"area":"agents"},{"id":"2603.02002","title":"MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials","created":"2026-03-02","cited_by_count":7,"area":null},{"id":"2302.08261","title":"Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey","created":"2023-02-16","cited_by_count":6,"area":null},{"id":"2406.03794","title":"Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models","created":"2024-06-06","cited_by_count":6,"area":null},{"id":"2406.16853","title":"GeoMFormer: A General Architecture for Geometric Molecular Representation Learning","created":"2024-06-24","cited_by_count":6,"area":null},{"id":"2506.09398","title":"Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames","created":"2025-06-11","cited_by_count":6,"area":null},{"id":"2304.03724","title":"GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising","created":"2023-03-28","cited_by_count":5,"area":null},{"id":"2309.16519","title":"AtomSurf : Surface Representation for Learning on Protein Structures","created":"2023-09-28","cited_by_count":5,"area":null},{"id":"2502.08975","title":"Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities","created":"2025-02-13","cited_by_count":5,"area":null},{"id":"2310.08677","title":"GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts","created":"2023-10-12","cited_by_count":4,"area":null},{"id":"2406.13265","title":"Molecule Graph Networks with Many-body Equivariant Interactions","created":"2024-06-19","cited_by_count":3,"area":null},{"id":"2505.02247","title":"RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation","created":"2025-05-04","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":"2601.21985","title":"Elign: Equivariant Diffusion Model Alignment from Foundational Machine Learning Force Fields","created":"2026-01-29","cited_by_count":3,"area":"diffusion"},{"id":"2212.11317","title":"End-to-end AI framework for interpretable prediction of molecular and crystal properties","created":"2022-12-21","cited_by_count":2,"area":null},{"id":"2306.11768","title":"Geometric Deep Learning for Structure-Based Drug Design: A Survey","created":"2023-06-20","cited_by_count":2,"area":null},{"id":"2405.16511","title":"SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning","created":"2024-05-26","cited_by_count":2,"area":null},{"id":"2503.10489","title":"Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling","created":"2025-03-13","cited_by_count":2,"area":null},{"id":"2312.14936","title":"PerCNet: Periodic Complete Representation for Crystal Graphs","created":"2023-12-03","cited_by_count":1,"area":null},{"id":"2406.08709","title":"Introducing Diminutive Causal Structure into Graph Representation Learning","created":"2024-06-13","cited_by_count":1,"area":null},{"id":"2407.02263","title":"FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields","created":"2024-07-02","cited_by_count":1,"area":null},{"id":"2504.04770","title":"Bidirectional Hierarchical Protein Multi-Modal Representation Learning","created":"2025-04-07","cited_by_count":1,"area":null},{"id":"2505.05877","title":"Multi-Modal Molecular Representation Learning via Structure Awareness","created":"2025-05-09","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":"2507.09043","title":"GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation","created":"2025-07-11","cited_by_count":1,"area":null},{"id":"2508.01195","title":"BSL: A Unified and Generalizable Multitask Learning Platform for Virtual Drug Discovery from Design to Synthesis","created":"2025-08-02","cited_by_count":1,"area":null},{"id":"2305.16540","title":"A Score-Based Model for Learning Neural Wavefunctions","created":"2023-05-25","cited_by_count":0,"area":null},{"id":"2311.17134","title":"GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks","created":"2023-11-28","cited_by_count":0,"area":null},{"id":"2403.14736","title":"NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks","created":"2024-03-21","cited_by_count":0,"area":null},{"id":"2405.20358","title":"Medication Recommendation via Dual Molecular Modalities and Multi-Step Enhancement","created":"2024-05-30","cited_by_count":0,"area":null},{"id":"2410.13713","title":"CrystalX: High-accuracy Crystal Structure Analysis Using Deep Learning","created":"2024-10-17","cited_by_count":0,"area":null},{"id":"2501.12434","title":"ConfRetro: a 3D-aware template-free method for enhancing retrosynthesis via molecular conformer information","created":"2025-01-21","cited_by_count":0,"area":null},{"id":"2507.18603","title":"Demystify Protein Generation with Hierarchical Conditional Diffusion Models","created":"2025-07-24","cited_by_count":0,"area":"diffusion"},{"id":"2510.07289","title":"MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2510.16780","title":"3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding","created":"2025-10-19","cited_by_count":0,"area":null},{"id":"2601.02213","title":"Quantized SO(3)-Equivariant Graph Neural Networks for Efficient Molecular Property Prediction","created":"2026-01-05","cited_by_count":0,"area":null},{"id":"2602.00862","title":"Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs","created":"2026-01-31","cited_by_count":0,"area":null},{"id":"2606.01595","title":"Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.20906","title":"MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2607.01408","title":"Spin-Weighted Spherical Harmonics Enable Complete and Scalable $\\mathrm{E}(3)$-Equivariant Networks","created":"2026-07-01","cited_by_count":0,"area":null}],"next_cursor":null}