P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials
Abstract
Ensemble method is considered the gold standard for uncertainty quantification (UQ) in machine learning interatomic potentials (MLIPs). However, their high computational cost can limit its practicality. Alternative techniques, such as Monte Carlo dropout and deep kernel learning, have been proposed to improve computational efficiency; however, some of these methods cannot be applied to already trained models and may affect the prediction accuracy. In this paper, we propose a simple and efficient post-hoc framework for UQ that leverages the descriptor of a trained graph neural network potential to estimate residual errors. We refer to this method as post-hoc descriptor-based residual uncertainty modeling (P-DRUM). P-DRUM models the discrepancy between MLIP predictions and ground truth values, allowing these residuals to act as proxies for prediction uncertainty. We explore multiple variants of P-DRUM and benchmark them against established UQ methods, evaluating both their effectiveness and limitations.
Cite
@article{arxiv.2509.02927,
title = {P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials},
author = {Shih-Peng Huang and Nontawat Charoenphakdee and Yuta Tsuboi and Yong-Bin Zhuang and Wenwen Li},
journal= {arXiv preprint arXiv:2509.02927},
year = {2025}
}
Comments
Accepted to 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: Machine Learning and the Physical Sciences (ML4PS)