{"total":26,"rows":[{"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":"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":"2602.19411","title":"MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry","created":"2026-02-23","cited_by_count":9,"area":null},{"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":"2412.15455","title":"Learning charges and long-range interactions from energies and forces","created":"2024-12-19","cited_by_count":7,"area":null},{"id":"2510.00224","title":"Learning from the electronic structure of molecules across the periodic table","created":"2025-09-30","cited_by_count":7,"area":null},{"id":"2506.04055","title":"chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations","created":"2025-06-04","cited_by_count":6,"area":null},{"id":"2509.14961","title":"Spectral/Spatial Tensor Atomic Cluster Expansion with Universal Embeddings in Cartesian Space","created":"2025-09-18","cited_by_count":6,"area":null},{"id":"2506.02023","title":"DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials","created":"2025-05-28","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":"2503.17949","title":"Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution","created":"2025-03-23","cited_by_count":4,"area":null},{"id":"2502.04668","title":"Machine-Learning Interatomic Potentials for Long-Range Systems","created":"2025-02-07","cited_by_count":2,"area":null},{"id":"2512.10989","title":"Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces","created":"2025-12-08","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":"2605.05746","title":"Polarizable atomic multipoles for learning long-range electrostatics","created":"2026-05-07","cited_by_count":2,"area":null},{"id":"2608.13506","title":"Equivariant learning of a transferable three-dimensional classical density functional","created":"2026-08-13","cited_by_count":2,"area":null},{"id":"2510.13055","title":"Reciprocal Space Attention for Learning Long-Range Interactions","created":"2025-10-15","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":"2605.25710","title":"Machine Learning Multiscale Interactions","created":"2026-05-25","cited_by_count":1,"area":null},{"id":"2504.05170","title":"SSLFusion: Scale & Space Aligned Latent Fusion Model for Multimodal 3D Object Detection","created":"2025-04-07","cited_by_count":0,"area":null},{"id":"2505.14606","title":"Electrostatics from Laplacian Eigenbasis for Neural Network Interatomic Potentials","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2508.10841","title":"Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2509.22028","title":"MCGM: Multi-stage Clustered Global Modeling for Long-range Interactions in Molecules","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2512.18029","title":"Long-range electrostatics for machine learning interatomic potentials is easier than we thought","created":"2025-12-19","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":"2609.01871","title":"Latent unified smooth Hamiltonians for excited state chemistry","created":"2026-09-01","cited_by_count":0,"area":null}],"next_cursor":null}