# Automated discovery of a robust interatomic potential for aluminum

arXiv: https://arxiv.org/abs/2003.04934 · HTML: https://mlsanity.com/paper/2003.04934/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls](https://mlsanity.com/paper/2212.07959/) (2022)
- [Data efficiency and extrapolation trends in neural network interatomic potentials](https://mlsanity.com/paper/2302.05823/) (2023)
- [Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory](https://mlsanity.com/paper/2404.12367/) (2024)
- [chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations](https://mlsanity.com/paper/2506.04055/) (2025)
- [Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties](https://mlsanity.com/paper/2210.08047/) (2022)
- [Accurate melting point prediction through autonomous physics-informed learning](https://mlsanity.com/paper/2306.13345/) (2023)
- [Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements](https://mlsanity.com/paper/2502.13522/) (2025)
- [Spline-based neural network interatomic potentials: blending classical and machine learning models](https://mlsanity.com/paper/2310.02904/) (2023)
- [Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO](https://mlsanity.com/paper/2403.00259/) (2024)
- [Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials](https://mlsanity.com/paper/2503.14293/) (2025)
- [Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory](https://mlsanity.com/paper/2511.10561/) (2025)

## Abstract (arXiv)

Accuracy of molecular dynamics simulations depends crucially on the interatomic potential used to generate forces. The gold standard would be first-principles quantum mechanics (QM) calculations, but these become prohibitively expensive at large simulation scales. Machine learning (ML) based potentials aim for faithful emulation of QM at drastically reduced computational cost. The accuracy and robustness of an ML potential is primarily limited by the quality and diversity of the training dataset. Using the principles of active learning (AL), we present a highly automated approach to dataset construction. The strategy is to use the ML potential under development to sample new atomic configurations and, whenever a configuration is reached for which the ML uncertainty is sufficiently large, collect new QM data. Here, we seek to push the limits of automation, removing as much expert knowledge from the AL process as possible. All sampling is performed using MD simulations starting from an initially disordered configuration, and undergoing non-equilibrium dynamics as driven by time-varying applied temperatures. We demonstrate this approach by building an ML potential for aluminum (ANI-Al). After many AL iterations, ANI-Al teaches itself to predict properties like the radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. To demonstrate transferability, we perform a 1.3M atom shock simulation, and show that ANI-Al predictions agree very well with DFT calculations on local atomic environments sampled from the nonequilibrium dynamics. Interestingly, the configurations appearing in shock appear to have been well sampled in the AL training dataset, in a way that we illustrate visually.