无需重新训练，用原子描述符估算机器学习势能的预测误差。
P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials
- 后处理框架，基于已训练模型的原子描述符建模残差误差。
- 在多种测试场景下，与传统方法相比保持相近的不确定性估计精度。
- 适合已部署的机器学习势能模型，快速添加不确定性评估能力。
集成方法被认为是机器学习原子间势能（MLIPs）中不确定性量化（UQ）的金标准，但其高计算成本限制了实际应用。已有替代方法如蒙特卡洛丢弃和深度核学习虽提升了效率，却存在无法用于已训练模型或影响预测精度的问题。本文提出一种简单高效的后处理不确定性量化框架——基于描述符的残差不确定性建模（P-DRUM），利用已训练图神经网络势能的原子描述符来估计预测残差。该方法将残差作为预测不确定性的代理指标，并探索多种变体，在多个基准上对比主流UQ方法，评估其有效性与局限性。
原文摘要 · Abstract (English)
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.
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