mlsanityData to 14 Oct 2026

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

arXiv 2212.05517 ↗First posted 11 Dec 2022physics.chem-ph stat.MLcited by 29

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Abstract (arXiv)

SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks as well as a PyTorch implementation of molecular dynamics. An optional integration with PyTorch Lightning and the Hydra configuration framework powers a flexible command-line interface. This makes SchNetPack 2.0 easily extendable with custom code and ready for complex training task such as generation of 3d molecular structures.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Builds on 36 of its 87 references that are in the map. First posted on arXiv 11 Dec 2022.

Builds on

36 of 87 references.

References · 36

  1. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
  4. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  5. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 664
  6. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 571
  7. 2020Directional Message Passing for Molecular Graphscited by 561
  8. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  9. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 441
  10. 20183D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Datacited by 354
  11. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 256
  12. 2020Machine Learning Force Fieldscited by 207
24 more
  1. 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 200
  2. 2018Constrained Graph Variational Autoencoders for Molecule Designcited by 185
  3. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 172
  4. 2017VAMPnets: Deep learning of molecular kineticscited by 136
  5. 2019Deep neural network solution of the electronic Schr"odinger equationcited by 127
  6. 2019Machine learning for molecular simulationcited by 110
  7. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 104
  8. 2021Inverse design of 3d molecular structures with conditional generative neural networkscited by 93
  9. 2019Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctionscited by 73
  10. 2016By-passing the Kohn-Sham equations with machine learningcited by 67
  11. 2021SE(3)-equivariant prediction of molecular wavefunctions and electronic densitiescited by 67
  12. 2019Equivariant Flows: sampling configurations for multi-body systems with symmetric energiescited by 57
  13. 2020Symmetry-Aware Actor-Critic for 3D Molecular Designcited by 55
  14. 2020TorchMD: A deep learning framework for molecular simulationscited by 55
  15. 2020Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physicscited by 36
  16. 2017Machine Learning Molecular Dynamics for the Simulation of Infrared Spectracited by 30
  17. 2020Predicting molecular dipole moments by combining atomic partial charges and atomic dipolescited by 18
  18. 202086 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracycited by 15
  19. 2020Machine learning of solvent effects on molecular spectra and reactionscited by 14
  20. 2022Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulationscited by 9
  21. 2020Autonomous robotic nanofabrication with reinforcement learningcited by 8
  22. 2018Band gap prediction for large organic crystal structures with machine learningcited by 5
  23. 2020Machine learning and excited-state molecular dynamicscited by 2
  24. 2022Automatic Identification of Chemical Moietiescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 8; 2025: 6; 2026: 6.

29 papers.

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  1. 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentialscited by 54
  2. 2023Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Spacecited by 41
  3. 2023Learning invariant representations of time-homogeneous stochastic dynamical systemscited by 17
  4. 2025High-performance training and inference for deep equivariant interatomic potentialscited by 17
  5. 2024TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulationscited by 15
  6. 2024Neural Conditional Probability for Uncertainty Quantificationcited by 14
  7. 2024chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physicscited by 12
  8. 2023Data efficiency and extrapolation trends in neural network interatomic potentialscited by 10
  9. 2023MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflowscited by 10
  10. 2024Molecular relaxation by reverse diffusion with time step predictioncited by 9
  11. 2024Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Featurescited by 7
  12. 2024FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentialscited by 6
17 more
  1. 2025OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytescited by 5
  2. 2025Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systemscited by 5
  3. 2025HollowFlow: Efficient Sample Likelihood Evaluation using Hollow Message Passingcited by 5
  4. 2023Gradual Optimization Learning for Conformational Energy Minimizationcited by 4
  5. 2025CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learningcited by 3
  6. 2023Stress and heat flux via automatic differentiationcited by 1
  7. 2023Multiscale Hodge Scattering Networks for Data Analysiscited by 1
  8. 2026Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forcescited by 1
  9. 2026Generative Pseudo-Force Fields for Molecular Generationcited by 1
  10. 2026Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulationscited by 1
  11. 2023Chemellia: An Ecosystem for Atomistic Scientific Machine Learningcited by 0
  12. 2024Infrared Spectra Prediction for Diazo Groups Utilizing a Machine Learning Approach with Structural Attention Mechanismcited by 0
  13. 2024it Asparagus: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfacescited by 0
  14. 2025A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectorscited by 0
  15. 2026GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Taskscited by 0
  16. 2026Temperature-Driven Sequential Modeling for the Prediction of Annual Power Conversion Efficiency Profiles of Organic Photovoltaic Materials: Douala Case Studycited by 0
  17. 2026The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learningcited by 0