{"total":37,"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":"2102.03150","title":"Equivariant message passing for the prediction of tensorial properties and molecular spectra","created":"2021-02-05","cited_by_count":441,"area":null},{"id":"2105.00304","title":"SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects","created":"2021-05-01","cited_by_count":104,"area":null},{"id":"2205.14276","title":"So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems","created":"2022-05-28","cited_by_count":39,"area":null},{"id":"2112.03383","title":"Graph Neural Networks Accelerated Molecular Dynamics","created":"2021-12-06","cited_by_count":33,"area":null},{"id":"2209.05582","title":"Graph Neural Networks for Molecules","created":"2022-09-12","cited_by_count":21,"area":null},{"id":"2109.07421","title":"Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials","created":"2021-09-15","cited_by_count":15,"area":null},{"id":"2303.02216","title":"Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials","created":"2023-03-03","cited_by_count":11,"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":"2109.09569","title":"Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments","created":"2021-09-20","cited_by_count":9,"area":null},{"id":"2110.13721","title":"Geometric Transformer for End-to-End Molecule Properties Prediction","created":"2021-10-26","cited_by_count":8,"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":"2603.04523","title":"Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials","created":"2026-03-04","cited_by_count":4,"area":null},{"id":"2204.09831","title":"Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space","created":"2022-04-21","cited_by_count":3,"area":null},{"id":"2211.13853","title":"Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry","created":"2022-11-25","cited_by_count":3,"area":null},{"id":"2211.16443","title":"Synthetic data enable experiments in atomistic machine learning","created":"2022-11-29","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":"2102.02390","title":"A Universal Framework for Featurization of Atomistic Systems","created":"2021-02-04","cited_by_count":2,"area":null},{"id":"2212.12737","title":"Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence","created":"2022-12-24","cited_by_count":2,"area":null},{"id":"2404.17365","title":"Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials","created":"2024-04-26","cited_by_count":2,"area":null},{"id":"2406.12112","title":"Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks","created":"2024-06-17","cited_by_count":2,"area":null},{"id":"2412.20796","title":"FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs","created":"2024-12-30","cited_by_count":2,"area":null},{"id":"2106.02549","title":"Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems","created":"2021-06-04","cited_by_count":1,"area":null},{"id":"2202.02310","title":"EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators","created":"2022-02-04","cited_by_count":1,"area":null},{"id":"2209.07300","title":"Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction","created":"2022-09-15","cited_by_count":1,"area":null},{"id":"2209.11234","title":"Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering","created":"2022-09-15","cited_by_count":1,"area":null},{"id":"2305.11805","title":"PANNA 2.0: Efficient neural network interatomic potentials and new architectures","created":"2023-05-19","cited_by_count":1,"area":null},{"id":"2309.15126","title":"From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields","created":"2023-09-21","cited_by_count":1,"area":null},{"id":"2503.23515","title":"Optimal Invariant Bases for Atomistic Machine Learning","created":"2025-03-30","cited_by_count":1,"area":null},{"id":"2110.14374","title":"A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization","created":"2021-10-27","cited_by_count":0,"area":null},{"id":"2302.00374","title":"HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks","created":"2023-02-01","cited_by_count":0,"area":null},{"id":"2310.02904","title":"Spline-based neural network interatomic potentials: blending classical and machine learning models","created":"2023-10-04","cited_by_count":0,"area":null},{"id":"2312.01414","title":"Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water","created":"2023-12-03","cited_by_count":0,"area":null},{"id":"2402.15921","title":"Pretraining Strategy for Neural Potentials","created":"2024-02-24","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":"2606.10349","title":"Magnetic HIP-NN for spin dynamics in disordered itinerant magnets","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2607.28537","title":"Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets","created":"2026-07-30","cited_by_count":0,"area":null}],"next_cursor":null}