Model ensembles are simple and effective tools for estimating the prediction uncertainty of deep learning atomistic force fields. Despite this, widespread adoption of ensemble-based uncertainty quantification (UQ) techniques is limited by the high computational costs incurred by ensembles during both training and inference. In this work we leverage the cumulative distribution functions (CDFs) of per-sample errors obtained over the course of training to efficiently represent the model ensemble, and couple them with a distance-based similarity search in the model latent space. Using these tools, we develop a simple UQ metric (which we call LTAU) that leverages the strengths of ensemble-based techniques without requiring the evaluation of multiple models during either training or inference. As an initial test, we apply our method towards estimating the epistemic uncertainty in atomistic force fields (LTAU-FF) and demonstrate that it can be easily calibrated to accurately predict test errors on multiple datasets from the literature. We then illustrate the utility of LTAU-FF in two practical applications: 1) tuning the training-validation gap for an example dataset, and 2) predicting errors in relaxation trajectories on the OC20 IS2RS task. Though in this work we focus on the use of LTAU with deep learning atomistic force fields, we emphasize that it can be readily applied to any regression task, or any ensemble-generation technique, to provide a reliable and easy-to-implement UQ metric.

翻译：模型集成是估计深度学习原子力场预测不确定性的简单而有效的工具。然而，基于集成的不确定性量化技术因其在训练和推理过程中产生的高计算成本而限制了其广泛应用。本文利用训练过程中获得的每个样本误差的累积分布函数高效地表示模型集成，并将其与模型潜在空间中的基于距离的相似性搜索相结合。基于这些工具，我们提出了一种简单的不确定性量化指标（称为LTAU），该指标利用了集成技术的优势，而无需在训练或推理过程中评估多个模型。作为初步测试，我们将该方法应用于估计原子力场的认知不确定性（LTAU-FF），并证明其可以轻松校准以准确预测文献中多个数据集上的测试误差。随后，我们展示了LTAU-FF在两个实际应用中的实用性：1) 针对示例数据集调整训练-验证差距，以及2) 预测OC20 IS2RS任务中弛豫轨迹的误差。尽管本文聚焦于LTAU在深度学习原子力场中的应用，但需强调，该方法可便捷地应用于任何回归任务或任何集成生成技术，以提供可靠且易于实现的不确定性量化指标。