mlsanityData to 14 Oct 2026

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

arXiv 1712.03641 ↗First posted 11 Dec 2017physics.comp-ph cs.LG physics.chem-phcited by 79

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Abstract (arXiv)

Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient. On the other end, DeePMD-kit is interfaced with high-performance classical molecular dynamics and quantum (path-integral) molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus, upon training, the potential energy and force field models can be used to perform efficient molecular simulations for different purposes. As an example of the many potential applications of the package, we use DeePMD-kit to learn the interatomic potential energy and forces of a water model using data obtained from density functional theory. We demonstrate that the resulted molecular dynamics model reproduces accurately the structural information contained in the original model.

Key facts. Cited by 79 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2017.

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Citing papers per year: 2021: 6; 2022: 8; 2023: 14; 2024: 17; 2025: 22; 2026: 12.

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  1. 2023An Expert's Guide to Training Physics-informed Neural Networkscited by 126
  2. 2023Smooth, exact rotational symmetrization for deep learning on point cloudscited by 39
  3. 2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33
  4. 2024Cartesian atomic cluster expansion for machine learning interatomic potentialscited by 31
  5. 2024Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Samplingcited by 27
  6. 2025A practical guide to machine learning interatomic potentials -- Status and futurecited by 26
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  8. 2022Hyperactive Learning (HAL) for Data-Driven Interatomic Potentialscited by 17
  9. 2021Differentiable Physics: A Position Piececited by 12
  10. 2024chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physicscited by 12
  11. 2025FlashMD: long-stride, universal prediction of molecular dynamicscited by 12
  12. 2023XLB: A differentiable massively parallel lattice Boltzmann library in Pythoncited by 11
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  1. 2023MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflowscited by 10
  2. 2025A universal augmentation framework for long-range electrostatics in machine learning interatomic potentialscited by 9
  3. 2023M2Hub: Unlocking the Potential of Machine Learning for Materials Discoverycited by 8
  4. 2024Learning local equivariant representations for quantum operatorscited by 8
  5. 2025Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modelingcited by 8
  6. 2025Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulationscited by 8
  7. 2025Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scalecited by 8
  8. 2023Completeness of Atomic Structure Representationscited by 7
  9. 2024Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuningcited by 6
  10. 2025chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulationscited by 6
  11. 2025Spectral/Spatial Tensor Atomic Cluster Expansion with Universal Embeddings in Cartesian Spacecited by 6
  12. 2021Symmetry-adapted graph neural networks for constructing molecular dynamics force fieldscited by 5
  13. 2023Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEscited by 5
  14. 2021BubbleNet: Inferring micro-bubble dynamics with semi-physics-informed deep learningcited by 4
  15. 2021Complex Spin Hamiltonian Represented by Artificial Neural Networkcited by 4
  16. 2026MatClaw: An Autonomous Code-First LLM Agent for End-to-End Materials Explorationcited by 4
  17. 2022Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalismcited by 3
  18. 2023Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systemscited by 3
  19. 2024Universal Machine Learning Kohn-Sham Hamiltonian for Materialscited by 3
  20. 2025AI Benchmark Democratization and Carpentrycited by 3
  21. 2022AI-coupled HPC Workflowscited by 2
  22. 2023Evaluating the Transferability of Machine-Learned Force Fields for Material Property Modelingcited by 2
  23. 2023Accurate melting point prediction through autonomous physics-informed learningcited by 2
  24. 2024Towards Harmonization of SO(3)-Equivariance and Expressiveness: a Hybrid Deep Learning Framework for Electronic-Structure Hamiltonian Predictioncited by 2
  25. 2024FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUscited by 2
  26. 2025AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potentialcited by 2
  27. 2025Data Fusion of Deep Learned Molecular Embeddings for Property Predictioncited by 2
  28. 2025An MLCommons Scientific Benchmarks Ontologycited by 2
  29. 2026Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passingcited by 2
  30. 2026Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blinkcited by 2
  31. 2022Edge-based Tensor prediction via graph neural networkscited by 1
  32. 2022Learning Regularized Positional Encoding for Molecular Predictioncited by 1
  33. 2022Neural DAEs: Constrained neural networkscited by 1
  34. 2022Utilising physics-guided deep learning to overcome data scarcitycited by 1
  35. 2023PANNA 2.0: Efficient neural network interatomic potentials and new architecturescited by 1
  36. 2024Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learningcited by 1
  37. 2025Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A reviewcited by 1
  38. 2021An Extendible, Graph-Neural-Network-Based Approach for Accurate Force Field Development of Large Flexible Organic Moleculescited by 0

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