# Committee neural network potentials control generalization errors and enable active learning

arXiv: https://arxiv.org/abs/2006.01541 · HTML: https://mlsanity.com/paper/2006.01541/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks](https://mlsanity.com/paper/2101.11588/) (2021)
- [Fast Uncertainty Estimates in Deep Learning Interatomic Potentials](https://mlsanity.com/paper/2211.09866/) (2022)
- [Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials](https://mlsanity.com/paper/2210.04225/) (2022)
- [Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials](https://mlsanity.com/paper/2312.01416/) (2023)
- [Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces](https://mlsanity.com/paper/2305.16325/) (2023)
- [Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators](https://mlsanity.com/paper/2402.13984/) (2024)
- [Knowing when to trust machine-learned interatomic potentials](https://mlsanity.com/paper/2605.00640/) (2026)
- [Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs](https://mlsanity.com/paper/2605.03964/) (2026)
- [Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs](https://mlsanity.com/paper/2605.13788/) (2026)
- [Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials](https://mlsanity.com/paper/2603.12183/) (2026)
- [Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water](https://mlsanity.com/paper/2312.01414/) (2023)
- [Ensemble Learning of Machine Learning Force Fields](https://mlsanity.com/paper/2403.17507/) (2024)
- [Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction](https://mlsanity.com/paper/2509.16266/) (2025)
- [Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields](https://mlsanity.com/paper/2607.14486/) (2026)
- [Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials](https://mlsanity.com/paper/2610.19630/) (2026)

## Abstract (arXiv)

It is well known in the field of machine learning that committee models improve accuracy, provide generalization error estimates, and enable active learning strategies. In this work, we adapt these concepts to interatomic potentials based on artificial neural networks. Instead of a single model, multiple models that share the same atomic environment descriptors yield an average that outperforms its individual members as well as a measure of the generalization error in the form of the committee disagreement. We not only use this disagreement to identify the most relevant configurations to build up the model's training set in an active learning procedure, but also monitor and bias it during simulations to control the generalization error. This facilitates the adaptive development of committee neural network potentials and their training sets, while keeping the number of ab initio calculations to a minimum. To illustrate the benefits of this methodology, we apply it to the development of a committee model for water in the condensed phase. Starting from a single reference ab initio simulation, we use active learning to expand into new state points and to describe the quantum nature of the nuclei. The final model, trained on 814 reference calculations, yields excellent results under a range of conditions, from liquid water at ambient and elevated temperatures and pressures to different phases of ice, and the air-water interface - all including nuclear quantum effects. This approach to committee models will enable the systematic development of robust machine learning models for a broad range of systems.