{"total":174,"rows":[{"id":"2210.01776","title":"DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking","created":"2022-10-04","cited_by_count":313,"area":null},{"id":"2302.03655","title":"Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs","created":"2023-02-07","cited_by_count":117,"area":null},{"id":"2406.04713","title":"FlowMM: Generating Materials with Riemannian Flow Matching","created":"2024-06-07","cited_by_count":109,"area":null},{"id":"2209.04934","title":"Clifford Neural Layers for PDE Modeling","created":"2022-09-08","cited_by_count":92,"area":null},{"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":"2502.12147","title":"Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction","created":"2025-02-17","cited_by_count":84,"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":"2304.02198","title":"EigenFold: Generative Protein Structure Prediction with Diffusion Models","created":"2023-04-05","cited_by_count":63,"area":"diffusion"},{"id":"2210.08566","title":"Theory for Equivariant Quantum Neural Networks","created":"2022-10-16","cited_by_count":62,"area":null},{"id":"2410.23405","title":"FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions","created":"2024-10-30","cited_by_count":53,"area":"diffusion"},{"id":"2309.02685","title":"Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation","created":"2023-09-06","cited_by_count":52,"area":"diffusion"},{"id":"2306.07473","title":"3D molecule generation by denoising voxel grids","created":"2023-06-13","cited_by_count":40,"area":null},{"id":"2410.24169","title":"The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains","created":"2024-10-31","cited_by_count":40,"area":null},{"id":"2302.06594","title":"Geometric Clifford Algebra Networks","created":"2023-02-13","cited_by_count":38,"area":null},{"id":"2302.11419","title":"Aligned Diffusion Schr\\\"odinger Bridges","created":"2023-02-22","cited_by_count":38,"area":null},{"id":"2308.10364","title":"SE(3) Equivariant Augmented Coupling Flows","created":"2023-08-20","cited_by_count":37,"area":null},{"id":"2401.10216","title":"Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products","created":"2024-01-18","cited_by_count":36,"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":"2304.10061","title":"Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size","created":"2023-04-20","cited_by_count":33,"area":null},{"id":"2402.07472","title":"Cartesian atomic cluster expansion for machine learning interatomic potentials","created":"2024-02-12","cited_by_count":31,"area":null},{"id":"2208.12590","title":"Ab-initio quantum chemistry with neural-network wavefunctions","created":"2022-08-26","cited_by_count":29,"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":"2503.09814","title":"A practical guide to machine learning interatomic potentials -- Status and future","created":"2025-03-12","cited_by_count":26,"area":null},{"id":"2305.14247","title":"Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science","created":"2023-05-23","cited_by_count":25,"area":null},{"id":"2309.15123","title":"Uncovering Neural Scaling Laws in Molecular Representation Learning","created":"2023-09-15","cited_by_count":23,"area":null},{"id":"2306.09375","title":"Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials","created":"2023-06-15","cited_by_count":22,"area":null},{"id":"2310.02299","title":"Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution","created":"2023-10-03","cited_by_count":20,"area":null},{"id":"2407.03300","title":"DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents","created":"2024-07-03","cited_by_count":20,"area":"diffusion"},{"id":"2405.03961","title":"Structure-based drug design by denoising voxel grids","created":"2024-05-07","cited_by_count":19,"area":"diffusion"},{"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":"2406.03164","title":"Topological Neural Networks go Persistent, Equivariant, and Continuous","created":"2024-06-05","cited_by_count":17,"area":null},{"id":"2306.00091","title":"A General Framework for Equivariant Neural Networks on Reductive Lie Groups","created":"2023-05-31","cited_by_count":16,"area":null},{"id":"2308.10436","title":"Approximately Equivariant Graph Networks","created":"2023-08-21","cited_by_count":16,"area":null},{"id":"2402.11459","title":"Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge","created":"2024-02-18","cited_by_count":16,"area":null},{"id":"2406.13864","title":"Evaluating representation learning on the protein structure universe","created":"2024-06-19","cited_by_count":16,"area":null},{"id":"2410.09667","title":"EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants","created":"2024-10-12","cited_by_count":16,"area":null},{"id":"2212.07347","title":"Lorentz group equivariant autoencoders","created":"2022-12-14","cited_by_count":15,"area":null},{"id":"2402.17660","title":"TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations","created":"2024-02-27","cited_by_count":15,"area":null},{"id":"2501.09009","title":"Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians","created":"2025-01-15","cited_by_count":15,"area":"distill"},{"id":"2503.23794","title":"Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks","created":"2025-03-31","cited_by_count":15,"area":null},{"id":"2506.13523","title":"The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products","created":"2025-06-16","cited_by_count":15,"area":null},{"id":"2209.14991","title":"Machine learning and invariant theory","created":"2022-09-29","cited_by_count":13,"area":null},{"id":"2302.14231","title":"CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling","created":"2023-02-28","cited_by_count":13,"area":null},{"id":"2312.05388","title":"Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials","created":"2023-12-08","cited_by_count":13,"area":null},{"id":"2305.15603","title":"Learning Lagrangian Fluid Mechanics with E($3$)-Equivariant Graph Neural Networks","created":"2023-05-24","cited_by_count":12,"area":null},{"id":"2509.21624","title":"HIP: Hessian Interatomic Potentials without derivatives","created":"2025-09-25","cited_by_count":12,"area":null},{"id":"2302.08780","title":"SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets","created":"2023-02-17","cited_by_count":11,"area":null},{"id":"2505.18817","title":"High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction","created":"2025-05-24","cited_by_count":11,"area":null},{"id":"2301.13724","title":"Towards fully covariant machine learning","created":"2023-01-31","cited_by_count":10,"area":null},{"id":"2307.05431","title":"Geometric Neural Diffusion Processes","created":"2023-07-11","cited_by_count":10,"area":"diffusion"},{"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":"2501.09064","title":"Generative diffusion model with inverse renormalization group flows","created":"2025-01-15","cited_by_count":10,"area":"diffusion"},{"id":"2503.07596","title":"Denoising Hamiltonian Network for Physical Reasoning","created":"2025-03-10","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":"2307.09337","title":"Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions","created":"2023-07-15","cited_by_count":9,"area":null},{"id":"2402.02681","title":"Equivariant Symmetry Breaking Sets","created":"2024-02-05","cited_by_count":9,"area":null},{"id":"2402.04278","title":"Gaussian Plane-Wave Neural Operator for Electron Density Estimation","created":"2024-02-05","cited_by_count":9,"area":null},{"id":"2405.19276","title":"A Recipe for Charge Density Prediction","created":"2024-05-29","cited_by_count":9,"area":null},{"id":"2406.12816","title":"Neural Approximate Mirror Maps for Constrained Diffusion Models","created":"2024-06-18","cited_by_count":9,"area":"diffusion"},{"id":"2307.05378","title":"M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery","created":"2023-06-14","cited_by_count":8,"area":null},{"id":"2406.12888","title":"A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction","created":"2024-06-03","cited_by_count":8,"area":null},{"id":"2407.06053","title":"Learning local equivariant representations for quantum operators","created":"2024-07-08","cited_by_count":8,"area":null},{"id":"2502.02513","title":"Diffusion Generative Modeling on Lie Group Representations","created":"2025-02-04","cited_by_count":8,"area":"diffusion"},{"id":"2506.02269","title":"A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models","created":"2025-06-02","cited_by_count":8,"area":null},{"id":"2507.01723","title":"SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space","created":"2025-07-02","cited_by_count":8,"area":"diffusion"},{"id":"2305.13316","title":"KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory","created":"2023-05-08","cited_by_count":7,"area":null},{"id":"2407.20471","title":"Relaxed Equivariant Graph Neural Networks","created":"2024-07-30","cited_by_count":7,"area":null},{"id":"2501.05580","title":"Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics","created":"2025-01-09","cited_by_count":7,"area":null},{"id":"2302.08381","title":"Fast evaluation of spherical harmonics with sphericart","created":"2023-02-16","cited_by_count":6,"area":null},{"id":"2310.04521","title":"Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras","created":"2023-10-06","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":"2509.13294","title":"Accelerating Protein Molecular Dynamics Simulation with DeepJump","created":"2025-09-16","cited_by_count":6,"area":null},{"id":"2310.13756","title":"Learning Interatomic Potentials at Multiple Scales","created":"2023-10-20","cited_by_count":5,"area":null},{"id":"2410.20516","title":"A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing","created":"2024-10-27","cited_by_count":5,"area":null},{"id":"2506.19862","title":"DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules","created":"2025-06-10","cited_by_count":5,"area":null},{"id":"2510.21542","title":"HollowFlow: Efficient Sample Likelihood Evaluation using Hollow Message Passing","created":"2025-10-24","cited_by_count":5,"area":null},{"id":"2301.13104","title":"Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models","created":"2023-01-30","cited_by_count":4,"area":null},{"id":"2302.12255","title":"Modeling Molecular Structures with Intrinsic Diffusion Models","created":"2023-02-23","cited_by_count":4,"area":"diffusion"},{"id":"2405.05722","title":"TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction","created":"2024-05-09","cited_by_count":4,"area":null},{"id":"2410.14621","title":"JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles","created":"2024-10-18","cited_by_count":4,"area":null},{"id":"2505.09203","title":"InvDesFlow-AL: active learning-based workflow for inverse design of functional materials","created":"2025-05-14","cited_by_count":4,"area":null},{"id":"2508.18949","title":"Energy-Based Flow Matching for Generating 3D Molecular Structure","created":"2025-08-26","cited_by_count":4,"area":"diffusion"},{"id":"2510.22984","title":"Equivariant Neural Networks for General Linear Symmetries on Lie Algebras","created":"2025-10-27","cited_by_count":4,"area":null},{"id":"2209.15567","title":"Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space","created":"2022-09-30","cited_by_count":3,"area":null},{"id":"2406.01552","title":"Tensor learning with orthogonal, Lorentz, and symplectic symmetries","created":"2024-06-03","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":"2509.25724","title":"Towards A Transferable Acceleration Method for Density Functional Theory","created":"2025-09-30","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":"2303.00351","title":"Leveraging SO(3)-steerable convolutions for pose-robust semantic segmentation in 3D medical data","created":"2023-03-01","cited_by_count":2,"area":null},{"id":"2306.00872","title":"Is novelty predictable?","created":"2023-06-01","cited_by_count":2,"area":null},{"id":"2402.10186","title":"Self-consistent Validation for Machine Learning Electronic Structure","created":"2024-02-15","cited_by_count":2,"area":null},{"id":"2409.07327","title":"Current Symmetry Group Equivariant Convolution Frameworks for Representation Learning","created":"2024-09-11","cited_by_count":2,"area":null},{"id":"2501.15055","title":"Group Ligands Docking to Protein Pockets","created":"2025-01-25","cited_by_count":2,"area":null},{"id":"2504.20940","title":"Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data","created":"2025-04-29","cited_by_count":2,"area":null},{"id":"2505.21539","title":"Equivariant Flow Matching for Point Cloud Assembly","created":"2025-05-24","cited_by_count":2,"area":"diffusion"},{"id":"2507.19799","title":"Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling","created":"2025-07-26","cited_by_count":2,"area":null},{"id":"2509.19877","title":"Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2601.12859","title":"Generating Cyclic Conformers with Flow Matching in Cremer-Pople Coordinates","created":"2026-01-19","cited_by_count":2,"area":null}],"next_cursor":"100"}