mlsanityData to 7 Oct 2026

Machine Learning Force Fields

arXiv 2010.07067 ↗First posted 14 Oct 2020physics.chem-ph stat.MLcited by 204

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Abstract (arXiv)

In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs.

Key facts. Cited by 204 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Oct 2020.

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Citing papers per year: 2021: 13; 2022: 25; 2023: 28; 2024: 39; 2025: 54; 2026: 45.

204 papers.

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  1. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  2. 2021Geometric Deep Learning on Molecular Representationscited by 113
  3. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  4. 2021Inverse design of 3d molecular structures with conditional generative neural networkscited by 93
  5. 2022Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulationscited by 92
  6. 2025Learning Smooth and Expressive Interatomic Potentials for Physical Property Predictioncited by 79
  7. 2022SELFIES and the future of molecular string representationscited by 76
  8. 2021SE(3)-equivariant prediction of molecular wavefunctions and electronic densitiescited by 67
  9. 2023Training neural operators to preserve invariant measures of chaotic attractorscited by 43
  10. 2024The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domainscited by 40
  11. 2022So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systemscited by 39
  12. 2022Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentialscited by 37
38 more
  1. 2022Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Pathscited by 36
  2. 2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33
  3. 2024Cartesian atomic cluster expansion for machine learning interatomic potentialscited by 31
  4. 2021Toward Explainable AI for Regression Modelscited by 30
  5. 2022Diffeomorphic Counterfactuals with Generative Modelscited by 29
  6. 2022SchNetPack 2.0: A neural network toolbox for atomistic machine learningcited by 29
  7. 2022Ab-initio quantum chemistry with neural-network wavefunctionscited by 28
  8. 2024Latent Ewald summation for machine learning of long-range interactionscited by 26
  9. 2021Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solidscited by 24
  10. 2025A practical guide to machine learning interatomic potentials -- Status and futurecited by 24
  11. 2022Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networkscited by 23
  12. 2024The dark side of the forces: assessing non-conservative force models for atomistic machine learningcited by 23
  13. 2025PET-MAD, a lightweight universal interatomic potential for advanced materials modelingcited by 23
  14. 2021Solving the electronic Schr"odinger equation for multiple nuclear geometries with weight-sharing deep neural networkscited by 22
  15. 2025LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Modelscited by 20
  16. 2024PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance samplingcited by 19
  17. 2023Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentialscited by 16
  18. 2022DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulationcited by 15
  19. 2023Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Reviewcited by 15
  20. 2025Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessianscited by 15
  21. 2023May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformationscited by 13
  22. 2024A predictive machine learning force field framework for liquid electrolyte developmentcited by 13
  23. 2024Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passingcited by 13
  24. 2025Force-Free Molecular Dynamics Through Autoregressive Equivariant Networkscited by 13
  25. 2025Equivariant Neural Diffusion for Molecule Generationcited by 13
  26. 2024Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Costcited by 12
  27. 2022Transfer learning for chemically accurate interatomic neural network potentialscited by 11
  28. 2023Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentialscited by 11
  29. 2025Reinforcement Fine-Tuning for Materials Designcited by 11
  30. 2026Pushing the limits of unconstrained machine-learned interatomic potentialscited by 11
  31. 2021Geometric Algebra Attention Networks for Small Point Cloudscited by 10
  32. 2023Data efficiency and extrapolation trends in neural network interatomic potentialscited by 10
  33. 2023MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflowscited by 10
  34. 2024Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theorycited by 10
  35. 2025How simple can you go? An off-the-shelf transformer approach to molecular dynamicscited by 10
  36. 2022Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulationscited by 9
  37. 2024Molecular relaxation by reverse diffusion with time step predictioncited by 9
  38. 2025ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitalscited by 9

50 most cited of 204 All 204 (JSON)