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Updated: Apr 15, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials.

Nicholas T Wimer¹, Juliane Mueller², Sebastien Hamel³

¹National Renewable Energy Laboratory, Golden, CO, USA. nwimer@nrel.gov.

Journal of Cheminformatics

|April 14, 2026

Summary

This summary is machine-generated.

Uncertainty quantification (UQ) for machine-learned interatomic potentials (ML-IAPs) is crucial. This study benchmarks different neural network potentials (NNPs) for UQ, finding aleatoric uncertainty competitive in data-rich areas but epistemic methods better in sparse regions.

Keywords:

Aleatoric Uncertainty Epistemic uncertainty Hyperparameter tuning Machine learned interatomic potential Neural network potential Uncertainty quantification

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Area of Science:

Computational materials science
Machine learning in chemistry and physics
Scientific computing

Background:

Machine-learned interatomic potentials (ML-IAPs) offer efficient and accurate alternatives to traditional methods.
Uncertainty quantification (UQ) is vital for ML-IAP applications like dataset curation and active learning.
Differentiating between epistemic and aleatoric uncertainty is key for reliable ML-IAP predictions.

Purpose of the Study:

To construct and benchmark various neural network potential (NNP) architectures for UQ.
To compare the performance of models predicting epistemic versus aleatoric uncertainty.
To assess uncertainty calibration error across different network architectures and datasets.

Main Methods:

Development and evaluation of multiple NNP architectures.
Benchmarking against standard ML-IAP datasets.
Analysis of epistemic and aleatoric uncertainty prediction performance.
Comparison of single-shot and ensemble-based UQ methods.

Main Results:

Aleatoric uncertainty from single-shot models is competitive with ensemble-based epistemic uncertainty in data-dense regions.
In data-sparse regions, aleatoric models tend to overpredict, while epistemic models tend to underpredict errors.
Model performance varies significantly based on the type of uncertainty quantified and data characteristics.

Conclusions:

The choice of UQ method is critical and depends heavily on data distribution and application requirements.
Careful evaluation of UQ methods against data characteristics ensures reliable probabilistic model performance.
Understanding the distinct behaviors of epistemic and aleatoric uncertainty is essential for advancing ML-IAP applications.