{"list":"other","total":87,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1801.07829","title":"Dynamic Graph CNN for Learning on Point Clouds","created":"2018-01-24","cited_by_count":2223},{"id":"1902.07153","title":"Simplifying Graph Convolutional Networks","created":"2019-02-19","cited_by_count":1477},{"id":"2006.04768","title":"Linformer: Self-Attention with Linear Complexity","created":"2020-06-08","cited_by_count":1474},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114},{"id":"1511.07289","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","created":"2015-11-23","cited_by_count":1070},{"id":"2209.04747","title":"Diffusion Models in Vision: A Survey","created":"2022-09-10","cited_by_count":1015},{"id":"1502.05767","title":"Automatic differentiation in machine learning: a survey","created":"2015-02-20","cited_by_count":855},{"id":"1509.09292","title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","created":"2015-09-30","cited_by_count":754},{"id":"1802.08219","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","created":"2018-02-22","cited_by_count":746},{"id":"2101.03164","title":"E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials","created":"2021-01-08","cited_by_count":708},{"id":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677},{"id":"1802.04364","title":"Junction Tree Variational Autoencoder for Molecular Graph Generation","created":"2018-02-12","cited_by_count":652},{"id":"2006.05205","title":"On the Bottleneck of Graph Neural Networks and its Practical Implications","created":"2020-06-09","cited_by_count":631},{"id":"1706.08566","title":"SchNet: A continuous-filter convolutional neural network for modeling quantum interactions","created":"2017-06-26","cited_by_count":571},{"id":"2004.05718","title":"Principal Neighbourhood Aggregation for Graph Nets","created":"2020-04-12","cited_by_count":478},{"id":"2111.14522","title":"Understanding over-squashing and bottlenecks on graphs via curvature","created":"2021-11-29","cited_by_count":478},{"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},{"id":"2102.03150","title":"Equivariant message passing for the prediction of tensorial properties and molecular spectra","created":"2021-02-05","cited_by_count":441},{"id":"2001.09382","title":"GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation","created":"2020-01-26","cited_by_count":305},{"id":"2101.03288","title":"How to Train Your Energy-Based Models","created":"2021-01-09","cited_by_count":274},{"id":"2106.10934","title":"GRAND: Graph Neural Diffusion","created":"2021-06-21","cited_by_count":258},{"id":"2204.05249","title":"Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics","created":"2022-04-11","cited_by_count":256},{"id":"2006.02425","title":"Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities","created":"2020-06-03","cited_by_count":250},{"id":"2006.13318","title":"A Note on Over-Smoothing for Graph Neural Networks","created":"2020-06-23","cited_by_count":249},{"id":"2303.10993","title":"A Survey on Oversmoothing in Graph Neural Networks","created":"2023-03-20","cited_by_count":247},{"id":"2010.09990","title":"The Open Catalyst 2020 (OC20) Dataset and Community Challenges","created":"2020-10-20","cited_by_count":246},{"id":"2002.06157","title":"Generalization and Representational Limits of Graph Neural Networks","created":"2020-02-14","cited_by_count":218},{"id":"2010.07067","title":"Machine Learning Force Fields","created":"2020-10-14","cited_by_count":207},{"id":"2006.10137","title":"MoFlow: An Invertible Flow Model for Generating Molecular Graphs","created":"2020-06-17","cited_by_count":185},{"id":"2202.02541","title":"TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials","created":"2022-02-05","cited_by_count":182},{"id":"2110.03336","title":"Frame Averaging for Invariant and Equivariant Network Design","created":"2021-10-07","cited_by_count":168},{"id":"2105.09016","title":"E(n) Equivariant Normalizing Flows","created":"2021-05-19","cited_by_count":165},{"id":"2106.06610","title":"Scalars are universal: Equivariant machine learning, structured like classical physics","created":"2021-06-11","cited_by_count":127},{"id":"2302.03655","title":"Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs","created":"2023-02-07","cited_by_count":117},{"id":"1710.10370","title":"Topology Adaptive Graph Convolutional Networks","created":"2017-10-28","cited_by_count":113},{"id":"1911.02792","title":"Machine learning for molecular simulation","created":"2019-11-07","cited_by_count":110},{"id":"2105.00304","title":"SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects","created":"2021-05-01","cited_by_count":104},{"id":"2205.06643","title":"The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials","created":"2022-05-13","cited_by_count":101},{"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},{"id":"1904.00314","title":"Molecular geometry prediction using a deep generative graph neural network","created":"2019-03-31","cited_by_count":91},{"id":"2301.09308","title":"On the Expressive Power of Geometric Graph Neural Networks","created":"2023-01-23","cited_by_count":90},{"id":"1801.09319","title":"Less is more: sampling chemical space with active learning","created":"2018-01-28","cited_by_count":82},{"id":"2209.10702","title":"SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials","created":"2022-09-21","cited_by_count":82},{"id":"2305.05577","title":"FAENet: Frame Averaging Equivariant GNN for Materials Modeling","created":"2023-04-28","cited_by_count":73},{"id":"2302.00600","title":"Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics","created":"2023-02-01","cited_by_count":72},{"id":"1812.01736","title":"Machine Learning of coarse-grained Molecular Dynamics Force Fields","created":"2018-12-04","cited_by_count":69},{"id":"2302.01170","title":"Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics","created":"2023-02-02","cited_by_count":66},{"id":"2007.08026","title":"OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features","created":"2020-07-15","cited_by_count":64},{"id":"2012.12106","title":"TorchMD: A deep learning framework for molecular simulations","created":"2020-12-22","cited_by_count":55},{"id":"2306.06482","title":"TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials","created":"2023-06-10","cited_by_count":54},{"id":"2310.03121","title":"OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials","created":"2023-10-04","cited_by_count":47},{"id":"2406.14426","title":"Transferable Boltzmann Generators","created":"2024-06-20","cited_by_count":46},{"id":"2207.12858","title":"Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials","created":"2022-07-25","cited_by_count":37},{"id":"2308.10364","title":"SE(3) Equivariant Augmented Coupling Flows","created":"2023-08-20","cited_by_count":37},{"id":"2401.10216","title":"Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products","created":"2024-01-18","cited_by_count":36},{"id":"2009.09457","title":"\"Hey, that's not an ODE\": Faster ODE Adjoints via Seminorms","created":"2020-09-20","cited_by_count":35},{"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},{"id":"2402.07472","title":"Cartesian atomic cluster expansion for machine learning interatomic potentials","created":"2024-02-12","cited_by_count":31},{"id":"1812.02706","title":"Coarse-Graining Auto-Encoders for Molecular Dynamics","created":"2018-12-06","cited_by_count":30},{"id":"2106.01138","title":"Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting","created":"2021-06-02","cited_by_count":26},{"id":"2003.00868","title":"Differentiable Molecular Simulations for Control and Learning","created":"2020-02-27","cited_by_count":25},{"id":"2101.11588","title":"Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks","created":"2021-01-27","cited_by_count":25},{"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},{"id":"2303.01569","title":"Chemically Transferable Generative Backmapping of Coarse-Grained Proteins","created":"2023-03-02","cited_by_count":14},{"id":"2301.08893","title":"Spatial Attention Kinetic Networks with E(n)-Equivariance","created":"2023-01-21","cited_by_count":11},{"id":"2408.00165","title":"Non-convolutional Graph Neural Networks","created":"2024-07-31","cited_by_count":11},{"id":"2404.10935","title":"Molecular relaxation by reverse diffusion with time step prediction","created":"2024-04-16","cited_by_count":9},{"id":"2201.08110","title":"NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic","created":"2022-01-20","cited_by_count":8},{"id":"2406.13112","title":"Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning","created":"2024-06-18","cited_by_count":7},{"id":"2102.12648","title":"Stochastic Aggregation in Graph Neural Networks","created":"2021-02-25","cited_by_count":6},{"id":"1909.07903","title":"Graph Nets for Partial Charge Prediction","created":"2019-09-17","cited_by_count":5},{"id":"2307.07085","title":"Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond","created":"2023-07-13","cited_by_count":5},{"id":"2311.03094","title":"Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks","created":"2023-11-06","cited_by_count":5},{"id":"2302.06758","title":"EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment","created":"2023-02-14","cited_by_count":4},{"id":"2010.01196","title":"End-to-End Differentiable Molecular Mechanics Force Field Construction","created":"2020-10-02","cited_by_count":3},{"id":"2404.00050","title":"Grappa -- A Machine Learned Molecular Mechanics Force Field","created":"2024-03-25","cited_by_count":3},{"id":"2408.12625","title":"Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations","created":"2024-08-17","cited_by_count":2}],"next_cursor":null}