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

arXiv: https://arxiv.org/abs/1810.11890 · HTML: https://mlsanity.com/paper/1810.11890/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2018.

## Built on by (most cited first, 30 of 37)

- [Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting](https://mlsanity.com/paper/2106.01138/) (2021)
- [Fast Uncertainty Estimates in Deep Learning Interatomic Potentials](https://mlsanity.com/paper/2211.09866/) (2022)
- [Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials](https://mlsanity.com/paper/2312.01416/) (2023)
- [DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation](https://mlsanity.com/paper/2208.08236/) (2022)
- [Accurate machine learning force fields via experimental and simulation data fusion](https://mlsanity.com/paper/2308.09142/) (2023)
- [Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls](https://mlsanity.com/paper/2212.07959/) (2022)
- [Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments](https://mlsanity.com/paper/2109.09569/) (2021)
- [Learning Macroscopic Dynamics from Partial Microscopic Observations](https://mlsanity.com/paper/2410.23938/) (2024)
- [Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs](https://mlsanity.com/paper/2605.03964/) (2026)
- [Active Learning for Saddle Point Calculation](https://mlsanity.com/paper/2108.04698/) (2021)
- [Active Learning in Physics: From 101, to Progress, and Perspective](https://mlsanity.com/paper/2307.03899/) (2023)
- [Mitigating distribution shift in machine learning-augmented hybrid simulation](https://mlsanity.com/paper/2401.09259/) (2024)
- [Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements](https://mlsanity.com/paper/2502.13522/) (2025)
- [Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs](https://mlsanity.com/paper/2605.13788/) (2026)
- [Active Learning for the Optimal Design of Multinomial Classification in Physics](https://mlsanity.com/paper/2109.08612/) (2021)
- [Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering](https://mlsanity.com/paper/2209.11234/) (2022)
- [When does deep learning fail and how to tackle it? A critical analysis on polymer sequence-property surrogate models](https://mlsanity.com/paper/2210.06622/) (2022)
- [Physics-informed active learning for accelerating quantum chemical simulations](https://mlsanity.com/paper/2404.11811/) (2024)
- [Bounds on the Generalization Error in Active Learning](https://mlsanity.com/paper/2409.09078/) (2024)
- [Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning](https://mlsanity.com/paper/2409.11808/) (2024)
- [Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials](https://mlsanity.com/paper/2410.20398/) (2024)
- [DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials](https://mlsanity.com/paper/2206.10093/) (2022)
- [Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression](https://mlsanity.com/paper/2210.15124/) (2022)
- [Uncertainty Quantification in Deep Neural Networks through Statistical Inference on Latent Space](https://mlsanity.com/paper/2305.10840/) (2023)
- [Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water](https://mlsanity.com/paper/2312.01414/) (2023)
- [Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO](https://mlsanity.com/paper/2403.00259/) (2024)
- [Implicit Delta Learning of High Fidelity Neural Network Potentials](https://mlsanity.com/paper/2412.06064/) (2024)
- [Active Learning with Variational Quantum Circuits for Quantum Process Tomography](https://mlsanity.com/paper/2412.20925/) (2024)
- [CrySPAI: A new Crystal Structure Prediction Software Based on Artificial Intelligence](https://mlsanity.com/paper/2501.15838/) (2025)
- [Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments](https://mlsanity.com/paper/2501.16398/) (2025)

## 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.