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Machine learning for molecular simulation

arXiv 1911.02792 ↗First posted 7 Nov 2019physics.chem-ph cs.LG physics.comp-phcited by 109

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Abstract (arXiv)

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, coarse-grained molecular dynamics, the extraction of free energy surfaces and kinetics and generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into machine learning structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation.

Key facts. Cited by 109 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 28; 2023: 23; 2024: 15; 2025: 14; 2026: 13.

109 papers.

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  1. 2021Enhancing Computational Fluid Dynamics with Machine Learningcited by 137
  2. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  3. 2022Action Matching: Learning Stochastic Dynamics from Samplescited by 94
  4. 202314 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathoncited by 85
  5. 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentialscited by 53
  6. 2023A new perspective on building efficient and expressive 3D equivariant graph neural networkscited by 46
  7. 2021Physics-Guided Deep Learning for Dynamical Systems: A Surveycited by 43
  8. 2023Generalized Schr"odinger Bridge Matchingcited by 40
  9. 2022So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systemscited by 39
  10. 2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33
  11. 2021Toward Explainable AI for Regression Modelscited by 30
  12. 2022SchNetPack 2.0: A neural network toolbox for atomistic machine learningcited by 29
38 more
  1. 2023MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problemscited by 29
  2. 2022Ab-initio quantum chemistry with neural-network wavefunctionscited by 28
  3. 2021Learning neural network potentials from experimental data via Differentiable Trajectory Reweightingcited by 26
  4. 2021Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flowscited by 24
  5. 2022DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulationscited by 24
  6. 2022Equivariant Networks for Crystal Structurescited by 24
  7. 2025A practical guide to machine learning interatomic potentials -- Status and futurecited by 24
  8. 2022Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networkscited by 23
  9. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  10. 2022Graph Neural Networks for Moleculescited by 21
  11. 2022Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopycited by 19
  12. 2021Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulationscited by 18
  13. 2021Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networkscited by 18
  14. 2023Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformercited by 18
  15. 2022Descriptors for Machine Learning Model of Generalized Force Field in Condensed Matter Systemscited by 16
  16. 2023Constructing Custom Thermodynamics Using Deep Learningcited by 16
  17. 2024Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Modelscited by 14
  18. 2025Flow Matching Meets Biology and Life Science: A Surveycited by 14
  19. 2024Foundation Inference Models for Markov Jump Processescited by 13
  20. 2021Differentiable Physics: A Position Piececited by 12
  21. 2022Machine learning predictions for local electronic properties of disordered correlated electron systemscited by 12
  22. 2023The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligencecited by 12
  23. 2022Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfallscited by 11
  24. 2023Accelerating Molecular Graph Neural Networks via Knowledge Distillationcited by 11
  25. 2025How simple can you go? An off-the-shelf transformer approach to molecular dynamicscited by 10
  26. 2022Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulationscited by 9
  27. 2023Multilevel CNNs for Parametric PDEscited by 9
  28. 2024Morphological Symmetries in Roboticscited by 9
  29. 2024Molecular relaxation by reverse diffusion with time step predictioncited by 9
  30. 2022NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechaniccited by 8
  31. 2022Deep Neural Network Approximation of Invariant Functions through Dynamical Systemscited by 8
  32. 2024Parametric model reduction of mean-field and stochastic systems via higher-order action matchingcited by 8
  33. 2022Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fieldscited by 7
  34. 2025Advancing the Scientific Method with Large Language Models: From Hypothesis to Discoverycited by 7
  35. 2025MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modelingcited by 7
  36. 2022Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexescited by 6
  37. 2024Governing equation discovery of a complex system from snapshotscited by 6
  38. 2025chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulationscited by 6

50 most cited of 109 All 109 (JSON)