{"total":66,"rows":[{"id":"2410.12771","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models","created":"2024-10-16","cited_by_count":52,"area":null},{"id":"2502.03638","title":"SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models","created":"2025-02-05","cited_by_count":51,"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":"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":"2403.00485","title":"A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications","created":"2024-03-01","cited_by_count":28,"area":null},{"id":"2412.11569","title":"The dark side of the forces: assessing non-conservative force models for atomistic machine learning","created":"2024-12-16","cited_by_count":24,"area":null},{"id":"2512.04562","title":"LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models","created":"2025-12-04","cited_by_count":23,"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":"2410.21317","title":"MatExpert: Decomposing Materials Discovery by Mimicking Human Experts","created":"2024-10-26","cited_by_count":18,"area":null},{"id":"2412.09560","title":"Foundational Large Language Models for Materials Research","created":"2024-12-12","cited_by_count":18,"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":"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":"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":"2405.14253","title":"Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing","created":"2024-05-23","cited_by_count":13,"area":null},{"id":"2406.17295","title":"Less can be more for predicting properties with large language models","created":"2024-06-25","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":"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":"2405.19276","title":"A Recipe for Charge Density Prediction","created":"2024-05-29","cited_by_count":9,"area":null},{"id":"2407.20597","title":"Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks","created":"2024-07-30","cited_by_count":9,"area":null},{"id":"2405.03962","title":"AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion","created":"2024-05-07","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":"2402.02287","title":"Future Directions in the Theory of Graph Machine Learning","created":"2024-02-03","cited_by_count":6,"area":null},{"id":"2506.19482","title":"Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning","created":"2025-06-24","cited_by_count":6,"area":null},{"id":"2508.05762","title":"UniFFBench: Evaluating Universal Machine Learning Force Fields Against Experimental Measurements","created":"2025-08-07","cited_by_count":6,"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":"2508.20527","title":"Molecular Machine Learning in Chemical Process Design","created":"2025-08-28","cited_by_count":5,"area":null},{"id":"2406.06504","title":"Equivariant Neural Tangent Kernels","created":"2024-06-10","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":"2509.23156","title":"CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning","created":"2025-09-27","cited_by_count":4,"area":null},{"id":"2409.17852","title":"AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics","created":"2024-09-26","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":"2403.04106","title":"Understanding Biology in the Age of Artificial Intelligence","created":"2024-03-06","cited_by_count":2,"area":null},{"id":"2404.10746","title":"Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials","created":"2024-04-16","cited_by_count":2,"area":null},{"id":"2410.08131","title":"Deconstructing equivariant representations in molecular systems","created":"2024-10-10","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":"2502.15376","title":"Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks","created":"2025-02-21","cited_by_count":2,"area":null},{"id":"2503.20541","title":"Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations","created":"2025-03-26","cited_by_count":2,"area":null},{"id":"2507.00927","title":"Understanding Generalization in Node and Link Prediction","created":"2025-07-01","cited_by_count":2,"area":null},{"id":"2603.18389","title":"An SO(3)-equivariant reciprocal-space neural potential for long-range interactions","created":"2026-03-19","cited_by_count":2,"area":null},{"id":"2604.12140","title":"XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures","created":"2026-04-13","cited_by_count":2,"area":null},{"id":"2404.15979","title":"On the Fourier analysis in the SO(3) space : EquiLoPO Network","created":"2024-04-24","cited_by_count":1,"area":null},{"id":"2501.01811","title":"QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials","created":"2025-01-03","cited_by_count":1,"area":null},{"id":"2510.01022","title":"VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks","created":"2025-10-01","cited_by_count":1,"area":null},{"id":"2511.19263","title":"Solar-GECO: Perovskite Solar Cell Property Prediction with Geometric-Aware Co-Attention","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2601.00581","title":"AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules","created":"2026-01-02","cited_by_count":1,"area":null},{"id":"2602.02671","title":"MARA: Continuous SE(3)-Equivariant Attention for Molecular Force Fields","created":"2026-02-02","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":"2608.27429","title":"Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation","created":"2026-08-27","cited_by_count":1,"area":null},{"id":"2410.13873","title":"On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants","created":"2024-10-02","cited_by_count":0,"area":null},{"id":"2410.20317","title":"ProtScape: A molecular structure and energy-aware representation for protein conformation generation","created":"2024-10-27","cited_by_count":0,"area":null},{"id":"2411.03156","title":"Unleashing the power of novel conditional generative approaches for new materials discovery","created":"2024-11-05","cited_by_count":0,"area":null},{"id":"2412.02957","title":"3D Interaction Geometric Pre-training for Molecular Relational Learning","created":"2024-12-04","cited_by_count":0,"area":null},{"id":"2503.00443","title":"Stable and Accurate Orbital-Free DFT Powered by Machine Learning","created":"2025-03-01","cited_by_count":0,"area":null},{"id":"2503.14293","title":"Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials","created":"2025-03-18","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":"2506.19834","title":"A standard transformer and attention with linear biases for molecular conformer generation","created":"2025-06-24","cited_by_count":0,"area":null},{"id":"2509.00832","title":"Challenges in Non-Polymeric Crystal Structure Prediction: Why a Geometric, Permutation-Invariant Loss is Needed","created":"2025-08-31","cited_by_count":0,"area":null},{"id":"2605.19124","title":"Atomistic Modeling of Chemical Disorder in Materials: Bridging Conventional Methods and AI-Assisted Approaches","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2608.16097","title":"Unifying Graph Neural Networks Through a Common Layer Equation","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2608.31045","title":"Rotational Equivariance in Machine Learning: A Comprehensive Tutorial","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.36237","title":"Transversal Pooling Neural Networks","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.14862","title":"Blending experimental electron density information to protein-ligand interaction","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}