{"total":61,"rows":[{"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":"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":"2506.23971","title":"UMA: A Family of Universal Models for Atoms","created":"2025-06-30","cited_by_count":105,"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":"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":"2410.12771","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models","created":"2024-10-16","cited_by_count":52,"area":null},{"id":"2206.14331","title":"Spherical Channels for Modeling Atomic Interactions","created":"2022-06-29","cited_by_count":47,"area":null},{"id":"2303.04791","title":"Ewald-based Long-Range Message Passing for Molecular Graphs","created":"2023-03-08","cited_by_count":46,"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":"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":"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":"2211.16486","title":"AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials","created":"2022-11-29","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":"2310.02299","title":"Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution","created":"2023-10-03","cited_by_count":20,"area":null},{"id":"2311.00341","title":"The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture","created":"2023-11-01","cited_by_count":18,"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":"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":"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":"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":"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":"2405.03962","title":"AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion","created":"2024-05-07","cited_by_count":8,"area":null},{"id":"2410.16658","title":"Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent","created":"2024-10-22","cited_by_count":8,"area":"agents"},{"id":"2603.06567","title":"A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention","created":"2026-03-06","cited_by_count":8,"area":null},{"id":"2508.03162","title":"The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture","created":"2025-08-05","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":"2211.12020","title":"PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design","created":"2022-11-22","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":"2510.00027","title":"Learning Inter-Atomic Potentials without Explicit Equivariance","created":"2025-09-25","cited_by_count":5,"area":null},{"id":"2306.01589","title":"Transfer learning for atomistic simulations using GNNs and kernel mean embeddings","created":"2023-06-02","cited_by_count":4,"area":null},{"id":"2510.22938","title":"AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis","created":"2025-10-27","cited_by_count":4,"area":null},{"id":"2512.06987","title":"OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction","created":"2025-12-07","cited_by_count":4,"area":null},{"id":"2503.08674","title":"Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields","created":"2025-03-11","cited_by_count":3,"area":null},{"id":"2505.23086","title":"Equivariant Spherical Transformer for Efficient Molecular Modeling","created":"2025-05-29","cited_by_count":3,"area":null},{"id":"2307.10988","title":"On minimizing the training set fill distance in machine learning regression","created":"2023-07-20","cited_by_count":2,"area":null},{"id":"2406.14347","title":"$\\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials","created":"2024-06-20","cited_by_count":2,"area":null},{"id":"2410.24220","title":"Bridging Geometric States via Geometric Diffusion Bridge","created":"2024-10-31","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":"2601.19966","title":"Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink","created":"2026-01-27","cited_by_count":2,"area":null},{"id":"2310.01649","title":"On Training Derivative-Constrained Neural Networks","created":"2023-10-02","cited_by_count":1,"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":"2503.01227","title":"Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery","created":"2025-03-03","cited_by_count":1,"area":null},{"id":"2504.19538","title":"Towards Faster and More Compact Foundation Models for Molecular Property Prediction","created":"2025-04-28","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":"2512.02303","title":"Training Dynamics of Learning 3D-Rotational Equivariance","created":"2025-12-02","cited_by_count":1,"area":null},{"id":"2607.29158","title":"Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations","created":"2026-07-31","cited_by_count":1,"area":null},{"id":"2502.07293","title":"Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity","created":"2025-02-11","cited_by_count":0,"area":null},{"id":"2502.11085","title":"On the Importance of Pretraining Data Alignment for Atomic Property Prediction","created":"2025-02-16","cited_by_count":0,"area":null},{"id":"2502.15483","title":"MoMa: A Modular Deep Learning Framework for Material Property Prediction","created":"2025-02-21","cited_by_count":0,"area":null},{"id":"2505.22208","title":"LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models","created":"2025-05-28","cited_by_count":0,"area":null},{"id":"2510.22123","title":"Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modeling","created":"2025-10-25","cited_by_count":0,"area":null},{"id":"2602.10419","title":"Equivariant Evidential Deep Learning for Interatomic Potentials","created":"2026-02-11","cited_by_count":0,"area":null},{"id":"2602.19289","title":"AdsorbFlow: energy-conditioned flow matching enables fast and realistic adsorbate placement","created":"2026-02-22","cited_by_count":0,"area":"diffusion"},{"id":"2604.15380","title":"Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data","created":"2026-04-15","cited_by_count":0,"area":null},{"id":"2605.08988","title":"Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2606.15001","title":"Distilling latent electrostatics from foundation machine learning interatomic potentials","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2609.05233","title":"Hessian-Informed Conformation Augmentation for Potential Energy Surface Curvature in Machine Learning Interatomic Potentials","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2609.28782","title":"The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning","created":"2026-09-23","cited_by_count":0,"area":null}],"next_cursor":null}