{"total":256,"rows":[{"id":"2206.07697","title":"MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields","created":"2022-06-15","cited_by_count":474,"area":null},{"id":"2206.11990","title":"Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs","created":"2022-06-23","cited_by_count":264,"area":null},{"id":"2306.12059","title":"EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations","created":"2023-06-21","cited_by_count":204,"area":null},{"id":"2207.09453","title":"e3nn: Euclidean Neural Networks","created":"2022-07-18","cited_by_count":174,"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":"2205.06643","title":"The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials","created":"2022-05-13","cited_by_count":101,"area":null},{"id":"2206.08917","title":"The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts","created":"2022-06-17","cited_by_count":95,"area":null},{"id":"2210.07237","title":"Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations","created":"2022-10-13","cited_by_count":95,"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":"2402.08871","title":"Position: Topological Deep Learning is the New Frontier for Relational Learning","created":"2024-02-14","cited_by_count":66,"area":null},{"id":"2306.06482","title":"TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials","created":"2023-06-10","cited_by_count":54,"area":null},{"id":"2502.14546","title":"Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks","created":"2025-02-20","cited_by_count":53,"area":null},{"id":"2410.23179","title":"Does equivariance matter at scale?","created":"2024-10-30","cited_by_count":52,"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":"2310.03121","title":"OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials","created":"2023-10-04","cited_by_count":47,"area":null},{"id":"2306.01187","title":"Training neural operators to preserve invariant measures of chaotic attractors","created":"2023-06-01","cited_by_count":46,"area":null},{"id":"2410.22388","title":"ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation","created":"2024-10-29","cited_by_count":43,"area":null},{"id":"2310.02970","title":"Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space","created":"2023-10-04","cited_by_count":41,"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":"2310.16802","title":"From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction","created":"2023-10-25","cited_by_count":39,"area":null},{"id":"2302.04313","title":"Geometry-Complete Diffusion for 3D Molecule Generation and Optimization","created":"2023-02-08","cited_by_count":37,"area":"diffusion"},{"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":"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":"2212.05517","title":"SchNetPack 2.0: A neural network toolbox for atomistic machine learning","created":"2022-12-11","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":"2308.13212","title":"SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases","created":"2023-08-25","cited_by_count":27,"area":null},{"id":"2309.16342","title":"LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite","created":"2023-09-28","cited_by_count":27,"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":"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":"2310.10732","title":"MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design","created":"2023-10-16","cited_by_count":24,"area":null},{"id":"2309.01156","title":"Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics","created":"2023-09-03","cited_by_count":23,"area":null},{"id":"2211.09866","title":"Fast Uncertainty Estimates in Deep Learning Interatomic Potentials","created":"2022-11-17","cited_by_count":22,"area":null},{"id":"2302.05743","title":"Is Distance Matrix Enough for Geometric Deep Learning?","created":"2023-02-11","cited_by_count":22,"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":"2501.13986","title":"An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks","created":"2025-01-23","cited_by_count":22,"area":null},{"id":"2306.01474","title":"Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning","created":"2023-06-02","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":"2504.16068","title":"High-performance training and inference for deep equivariant interatomic potentials","created":"2025-04-22","cited_by_count":17,"area":null},{"id":"2208.08236","title":"DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation","created":"2022-08-17","cited_by_count":15,"area":null},{"id":"2310.02428","title":"EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations","created":"2023-10-03","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":"2405.15389","title":"Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing","created":"2024-05-24","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":"2210.17484","title":"The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science","created":"2022-10-31","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":"2309.16578","title":"Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning","created":"2023-09-28","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":"2404.07181","title":"A predictive machine learning force field framework for liquid electrolyte development","created":"2024-04-10","cited_by_count":13,"area":null},{"id":"2405.14253","title":"Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing","created":"2024-05-23","cited_by_count":13,"area":null},{"id":"2308.09142","title":"Accurate machine learning force fields via experimental and simulation data fusion","created":"2023-08-17","cited_by_count":12,"area":null},{"id":"2408.15852","title":"chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics","created":"2024-08-28","cited_by_count":12,"area":null},{"id":"2412.08541","title":"Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost","created":"2024-12-11","cited_by_count":12,"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":"2505.19350","title":"FlashMD: long-stride, universal prediction of molecular dynamics","created":"2025-05-25","cited_by_count":12,"area":null},{"id":"2306.14818","title":"Accelerating Molecular Graph Neural Networks via Knowledge Distillation","created":"2023-06-26","cited_by_count":11,"area":"distill"},{"id":"2402.04836","title":"On the Completeness of Invariant Geometric Deep Learning Models","created":"2024-02-07","cited_by_count":11,"area":null},{"id":"2502.01171","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","created":"2025-02-03","cited_by_count":11,"area":null},{"id":"2509.25486","title":"Scalable Boltzmann Generators for equilibrium sampling of large-scale materials","created":"2025-09-29","cited_by_count":11,"area":null},{"id":"2601.16195","title":"Pushing the limits of unconstrained machine-learned interatomic potentials","created":"2026-01-22","cited_by_count":11,"area":null},{"id":"2604.09130","title":"EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers","created":"2026-04-10","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":"2302.05823","title":"Data efficiency and extrapolation trends in neural network interatomic potentials","created":"2023-02-12","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":"2411.09678","title":"NeuralDEM -- Real-time Simulation of Industrial Particulate Flows","created":"2024-11-14","cited_by_count":10,"area":null},{"id":"2503.01431","title":"How simple can you go? An off-the-shelf transformer approach to molecular dynamics","created":"2025-03-03","cited_by_count":10,"area":null},{"id":"2506.04528","title":"Hierarchical Implicit Neural Emulators","created":"2025-06-05","cited_by_count":10,"area":null},{"id":"2212.06096","title":"Implicit Convolutional Kernels for Steerable CNNs","created":"2022-12-12","cited_by_count":9,"area":null},{"id":"2404.10935","title":"Molecular relaxation by reverse diffusion with time step prediction","created":"2024-04-16","cited_by_count":9,"area":null},{"id":"2409.17622","title":"Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs","created":"2024-09-26","cited_by_count":9,"area":null},{"id":"2507.14302","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","created":"2025-07-18","cited_by_count":9,"area":null},{"id":"2311.11362","title":"Symmetry-invariant quantum machine learning force fields","created":"2023-11-19","cited_by_count":8,"area":null},{"id":"2402.12335","title":"Image Super-resolution Inspired Electron Density Prediction","created":"2024-02-19","cited_by_count":8,"area":null},{"id":"2402.13984","title":"Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators","created":"2024-02-21","cited_by_count":8,"area":null},{"id":"2405.03962","title":"AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion","created":"2024-05-07","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":"2502.16284","title":"MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra","created":"2025-02-22","cited_by_count":8,"area":null},{"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":"2211.13408","title":"Graph Contrastive Learning for Materials","created":"2022-11-24","cited_by_count":7,"area":null},{"id":"2302.14770","title":"Completeness of Atomic Structure Representations","created":"2023-02-28","cited_by_count":7,"area":null},{"id":"2303.04124","title":"Wigner kernels: body-ordered equivariant machine learning without a basis","created":"2023-03-07","cited_by_count":7,"area":null},{"id":"2312.08307","title":"3DReact: Geometric deep learning for chemical reactions","created":"2023-12-13","cited_by_count":7,"area":null},{"id":"2406.13112","title":"Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning","created":"2024-06-18","cited_by_count":7,"area":null},{"id":"2408.08006","title":"Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents","created":"2024-08-15","cited_by_count":7,"area":null},{"id":"2408.17198","title":"Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features","created":"2024-08-30","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":"2601.22123","title":"Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics","created":"2026-01-29","cited_by_count":7,"area":null},{"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":"2308.11155","title":"Beyond MD17: the reactive xxMD dataset","created":"2023-08-22","cited_by_count":6,"area":null},{"id":"2311.09312","title":"H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing","created":"2023-11-15","cited_by_count":6,"area":null},{"id":"2405.01491","title":"FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials","created":"2024-05-02","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":"2411.04219","title":"Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction","created":"2024-11-06","cited_by_count":6,"area":null},{"id":"2412.18263","title":"High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces","created":"2024-12-24","cited_by_count":6,"area":null}],"next_cursor":"100"}