LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force
  Fields
Joshua A. Vita Amit Samanta Fei Zhou Vincenzo Lordi
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.