# Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials

arXiv: https://arxiv.org/abs/2210.04225 · HTML: https://mlsanity.com/paper/2210.04225/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 84 references that are in the map.
- First posted on arXiv 9 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials](https://mlsanity.com/paper/2101.03164/) (2021)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields](https://mlsanity.com/paper/2206.07697/) (2022)
- [Equivariant message passing for the prediction of tensorial properties and molecular spectra](https://mlsanity.com/paper/2102.03150/) (2021)
- [GemNet: Universal Directional Graph Neural Networks for Molecules](https://mlsanity.com/paper/2106.08903/) (2021)
- [Cormorant: Covariant Molecular Neural Networks](https://mlsanity.com/paper/1906.04015/) (2019)
- [Maximizing acquisition functions for Bayesian optimization](https://mlsanity.com/paper/1805.10196/) (2018)
- [DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics](https://mlsanity.com/paper/1712.03641/) (2017)
- [Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks](https://mlsanity.com/paper/2101.11588/) (2021)
- [Committee neural network potentials control generalization errors and enable active learning](https://mlsanity.com/paper/2006.01541/) (2020)

## Built on by (most cited first, 17 of 17)

- [A practical guide to machine learning interatomic potentials -- Status and future](https://mlsanity.com/paper/2503.09814/) (2025)
- [Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials](https://mlsanity.com/paper/2312.01416/) (2023)
- [BLIPs: Bayesian Learned Interatomic Potentials](https://mlsanity.com/paper/2508.14022/) (2025)
- [Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites](https://mlsanity.com/paper/2403.06955/) (2024)
- [Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes](https://mlsanity.com/paper/2603.22160/) (2026)
- [Knowing when to trust machine-learned interatomic potentials](https://mlsanity.com/paper/2605.00640/) (2026)
- [Enhanced sampling of robust molecular datasets with uncertainty-based collective variables](https://mlsanity.com/paper/2402.03753/) (2024)
- [Stress and heat flux via automatic differentiation](https://mlsanity.com/paper/2305.01401/) (2023)
- [Physics-Informed Weakly Supervised Learning for Interatomic Potentials](https://mlsanity.com/paper/2408.05215/) (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)
- [Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence](https://mlsanity.com/paper/2507.19438/) (2025)
- [Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes](https://mlsanity.com/paper/2311.05009/) (2023)
- [On the generalization ability of coarse-grained molecular dynamics models for non-equilibrium processes](https://mlsanity.com/paper/2409.11519/) (2024)
- [Implicit Delta Learning of High Fidelity Neural Network Potentials](https://mlsanity.com/paper/2412.06064/) (2024)
- [Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials](https://mlsanity.com/paper/2508.03405/) (2025)
- [Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights](https://mlsanity.com/paper/2605.10654/) (2026)

## Abstract (arXiv)

Data-driven interatomic potentials have emerged as a powerful class of surrogate models for it ab initio potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy. In generating accurate and transferable potentials the most time-consuming and arguably most important task is generating the training set, which still requires significant expert user input. To accelerate this process, this work presents it hyperactive learning (HAL), a framework for formulating an accelerated sampling algorithm specifically for the task of training database generation. The key idea is to start from a physically motivated sampler (e.g., molecular dynamics) and add a biasing term that drives the system towards high uncertainty and thus to unseen training configurations. Building on this framework, general protocols for building training databases for alloys and polymers leveraging the HAL framework will be presented. For alloys, ACE potentials for AlSi10 are created by fitting to a minimal HAL-generated database containing 88 configurations (32 atoms each) with fast evaluation times of <100 microsecond/atom/cpu-core. These potentials are demonstrated to predict the melting temperature with excellent accuracy. For polymers, a HAL database is built using ACE, able to determine the density of a long polyethylene glycol (PEG) polymer formed of 200 monomer units with experimental accuracy by only fitting to small isolated PEG polymers with sizes ranging from 2 to 32.