{"list":"other","total":57,"rows":[{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"2102.09672","title":"Improved Denoising Diffusion Probabilistic Models","created":"2021-02-18","cited_by_count":3761},{"id":"1606.08415","title":"Gaussian Error Linear Units (GELUs)","created":"2016-06-27","cited_by_count":3502},{"id":"1910.07467","title":"Root Mean Square Layer Normalization","created":"2019-10-16","cited_by_count":1480},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"id":"1801.07606","title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","created":"2018-01-22","cited_by_count":1102},{"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":"1906.01563","title":"Hamiltonian Neural Networks","created":"2019-06-04","cited_by_count":712},{"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":"1909.01377","title":"Deep Equilibrium Models","created":"2019-09-03","cited_by_count":615},{"id":"1810.01367","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","created":"2018-10-02","cited_by_count":594},{"id":"1706.08566","title":"SchNet: A continuous-filter convolutional neural network for modeling quantum interactions","created":"2017-06-26","cited_by_count":571},{"id":"2003.03123","title":"Directional Message Passing for Molecular Graphs","created":"2020-03-06","cited_by_count":561},{"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":"2003.04630","title":"Lagrangian Neural Networks","created":"2020-03-10","cited_by_count":398},{"id":"2106.08903","title":"GemNet: Universal Directional Graph Neural Networks for Molecules","created":"2021-06-02","cited_by_count":343},{"id":"1705.03341","title":"Stable Architectures for Deep Neural Networks","created":"2017-05-09","cited_by_count":341},{"id":"2206.11990","title":"Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs","created":"2022-06-23","cited_by_count":264},{"id":"2204.05249","title":"Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics","created":"2022-04-11","cited_by_count":256},{"id":"2010.07067","title":"Machine Learning Force Fields","created":"2020-10-14","cited_by_count":207},{"id":"2306.12059","title":"EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations","created":"2023-06-21","cited_by_count":204},{"id":"1710.10121","title":"Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations","created":"2017-10-27","cited_by_count":203},{"id":"1804.04272","title":"Deep Neural Networks Motivated by Partial Differential Equations","created":"2018-04-12","cited_by_count":187},{"id":"2302.03655","title":"Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs","created":"2023-02-07","cited_by_count":117},{"id":"2506.23971","title":"UMA: A Family of Universal Models for Atoms","created":"2025-06-30","cited_by_count":105},{"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":"2006.08591","title":"Monotone operator equilibrium networks","created":"2020-06-15","cited_by_count":100},{"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":"2502.12147","title":"Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction","created":"2025-02-17","cited_by_count":84},{"id":"1906.10033","title":"Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions","created":"2019-06-24","cited_by_count":73},{"id":"2106.14342","title":"Stabilizing Equilibrium Models by Jacobian Regularization","created":"2021-06-28","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":"1805.10369","title":"Stable Recurrent Models","created":"2018-05-25","cited_by_count":66},{"id":"2410.22570","title":"Orb: A Fast, Scalable Neural Network Potential","created":"2024-10-29","cited_by_count":66},{"id":"2204.08442","title":"Deep Equilibrium Optical Flow Estimation","created":"2022-04-18","cited_by_count":64},{"id":"2204.02782","title":"GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets","created":"2022-04-06","cited_by_count":61},{"id":"2103.01436","title":"ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations","created":"2021-03-02","cited_by_count":58},{"id":"2007.11412","title":"Coarse Graining Molecular Dynamics with Graph Neural Networks","created":"2020-07-22","cited_by_count":56},{"id":"2206.14331","title":"Spherical Channels for Modeling Atomic Interactions","created":"2022-06-29","cited_by_count":47},{"id":"2212.07492","title":"Machine Learning Coarse-Grained Potentials of Protein Thermodynamics","created":"2022-12-14","cited_by_count":37},{"id":"2102.07346","title":"On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers","created":"2021-02-15","cited_by_count":35},{"id":"2212.05517","title":"SchNetPack 2.0: A neural network toolbox for atomistic machine learning","created":"2022-12-11","cited_by_count":29},{"id":"2011.07125","title":"Better, Faster Fermionic Neural Networks","created":"2020-11-13","cited_by_count":28},{"id":"2112.00652","title":"Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids","created":"2021-12-01","cited_by_count":25},{"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},{"id":"2310.18278","title":"Navigating protein landscapes with a machine-learned transferable coarse-grained model","created":"2023-10-27","cited_by_count":23},{"id":"2503.23794","title":"Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks","created":"2025-03-31","cited_by_count":15},{"id":"2403.09560","title":"Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction","created":"2024-03-14","cited_by_count":12},{"id":"2505.19350","title":"FlashMD: long-stride, universal prediction of molecular dynamics","created":"2025-05-25","cited_by_count":12},{"id":"2604.09130","title":"EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers","created":"2026-04-10","cited_by_count":11},{"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},{"id":"2601.22123","title":"Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics","created":"2026-01-29","cited_by_count":7},{"id":"2406.03794","title":"Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models","created":"2024-06-06","cited_by_count":6},{"id":"2509.08734","title":"DEQuify your force field: More efficient simulations using deep equilibrium models","created":"2025-09-10","cited_by_count":3}],"next_cursor":null}