mlsanityData to 7 Oct 2026

Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation

arXiv 1810.11890 ↗First posted 28 Oct 2018physics.comp-ph cond-mat.mtrl-sci cs.LGcited by 37

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Abstract (arXiv)

An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Oct 2018.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 5; 2024: 9; 2025: 6; 2026: 6.

37 papers.

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  1. 2021Learning neural network potentials from experimental data via Differentiable Trajectory Reweightingcited by 26
  2. 2022Fast Uncertainty Estimates in Deep Learning Interatomic Potentialscited by 22
  3. 2023Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentialscited by 16
  4. 2022DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulationcited by 15
  5. 2023Accurate machine learning force fields via experimental and simulation data fusioncited by 12
  6. 2022Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfallscited by 11
  7. 2021Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Momentscited by 9
  8. 2024Learning Macroscopic Dynamics from Partial Microscopic Observationscited by 3
  9. 2026Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPscited by 3
  10. 2021Active Learning for Saddle Point Calculationcited by 2
  11. 2023Active Learning in Physics: From 101, to Progress, and Perspectivecited by 2
  12. 2024Mitigating distribution shift in machine learning-augmented hybrid simulationcited by 2
25 more
  1. 2025Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elementscited by 2
  2. 2026Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPscited by 2
  3. 2021Active Learning for the Optimal Design of Multinomial Classification in Physicscited by 1
  4. 2022Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineeringcited by 1
  5. 2022When does deep learning fail and how to tackle it? A critical analysis on polymer sequence-property surrogate modelscited by 1
  6. 2024Physics-informed active learning for accelerating quantum chemical simulationscited by 1
  7. 2024Bounds on the Generalization Error in Active Learningcited by 1
  8. 2024Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learningcited by 1
  9. 2024Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentialscited by 1
  10. 2022DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentialscited by 0
  11. 2022Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regressioncited by 0
  12. 2023Uncertainty Quantification in Deep Neural Networks through Statistical Inference on Latent Spacecited by 0
  13. 2023Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Watercited by 0
  14. 2024Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeOcited by 0
  15. 2024Implicit Delta Learning of High Fidelity Neural Network Potentialscited by 0
  16. 2024Active Learning with Variational Quantum Circuits for Quantum Process Tomographycited by 0
  17. 2025CrySPAI: A new Crystal Structure Prediction Software Based on Artificial Intelligencecited by 0
  18. 2025Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environmentscited by 0
  19. 2025Learning task-specific predictive models for scientific computingcited by 0
  20. 2025Migration as a Probe: A Generalizable Benchmark Framework for Specialist vs. Generalist Machine-Learned Force Fieldscited by 0
  21. 2025Active Learning for Machine Learning Driven Molecular Dynamicscited by 0
  22. 2026A Critical Examination of Active Learning Workflows in Materials Sciencecited by 0
  23. 2026Data-driven construction of machine-learning-based interatomic potentials for gas-surface scattering dynamics: the case of NO on graphitecited by 0
  24. 2026Active rejection enables reliable generalization of universal machine-learning interatomic potentialscited by 0
  25. 2026Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fieldscited by 0