mlsanityData to 7 Oct 2026

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics

arXiv 1707.09571 ↗First posted 30 Jul 2017physics.comp-ph cs.LG physics.chem-phcited by 213

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Abstract (arXiv)

We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is "first principle-based" in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DeePMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

Key facts. Cited by 213 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jul 2017.

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Citing papers per year: 2021: 31; 2022: 42; 2023: 31; 2024: 42; 2025: 44; 2026: 23.

213 papers.

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  1. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  2. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  3. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  4. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  5. 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
  6. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  7. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  8. 2022Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulationscited by 92
  9. 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 88
  10. 2021An Unconstrained Layer-Peeled Perspective on Neural Collapsecited by 87
  11. 2022One Transformer Can Understand Both 2D & 3D Molecular Datacited by 77
  12. 2024PirateNets: Physics-informed Deep Learning with Residual Adaptive Networkscited by 70
38 more
  1. 2022Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modelingcited by 65
  2. 2022Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problemscited by 47
  3. 2021Physics-Guided Deep Learning for Dynamical Systems: A Surveycited by 43
  4. 2023Smooth, exact rotational symmetrization for deep learning on point cloudscited by 39
  5. 2022Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal datacited by 37
  6. 2023Domain Agnostic Fourier Neural Operatorscited by 36
  7. 2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33
  8. 2023Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic sizecited by 33
  9. 2021Constructing Neural Network-Based Models for Simulating Dynamical Systemscited by 29
  10. 2022INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservationcited by 28
  11. 2021Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistrycited by 27
  12. 2021Differentiable sampling of molecular geometries with uncertainty-based adversarial attackscited by 25
  13. 2021On the Representation of Solutions to Elliptic PDEs in Barron Spacescited by 25
  14. 2022Partial Differential Equations Meet Deep Neural Networks: A Surveycited by 25
  15. 2022DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulationscited by 24
  16. 2022Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networkscited by 23
  17. 2022Fast Uncertainty Estimates in Deep Learning Interatomic Potentialscited by 22
  18. 2023Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+cited by 22
  19. 2023Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materialscited by 22
  20. 2023Harnessing the Power of Neural Operators with Automatically Encoded Conservation Lawscited by 22
  21. 2021Deep Molecular Representation Learning via Fusing Physical and Chemical Informationcited by 21
  22. 2022Graph Neural Networks for Moleculescited by 21
  23. 2021Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulationscited by 18
  24. 2023Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networkscited by 18
  25. 2024Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fieldscited by 18
  26. 2024Efficient mapping of phase diagrams with conditional Boltzmann Generatorscited by 18
  27. 2024Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discoverycited by 18
  28. 2021Machine learning nonequilibrium electron forces for adiabatic spin dynamicscited by 17
  29. 2021Solving time dependent Fokker-Planck equations via temporal normalizing flowcited by 17
  30. 2022Descriptors for Machine Learning Model of Generalized Force Field in Condensed Matter Systemscited by 16
  31. 2022DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulationcited by 15
  32. 2023Machine learning for phase ordering dynamics of charge density wavescited by 15
  33. 2021Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networkscited by 14
  34. 2021Error Estimates for the Deep Ritz Method with Boundary Penaltycited by 14
  35. 2021HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networkscited by 13
  36. 2024A predictive machine learning force field framework for liquid electrolyte developmentcited by 13
  37. 2024Probing the effects of broken symmetries in machine learningcited by 13
  38. 2022Machine learning predictions for local electronic properties of disordered correlated electron systemscited by 12

50 most cited of 213 All 213 (JSON)