Uncertainty Estimation in Atomistic Machine Learning: Calibration, Robustness, and Detection of Distribution Shift

Thu, 24 Sep 2026 00:00:00 +0000

Background

Uncertainty estimation is becoming increasingly important in scientific machine learning, especially when models are used to support decision-making in chemistry, materials, and molecular design. In atomistic systems, where data may be noisy, sparse, or shifted relative to the training distribution, a model that produces a single point estimate can be misleading. In such settings, understanding whether a prediction is reliable is often as important as the prediction itself.

A variety of uncertainty methods have been developed in deep learning, including ensembles, Bayesian approximations, calibration techniques, and evidential approaches. However, these methods do not always transfer cleanly to atomistic models, where structure, symmetry, local geometry, and domain shifts can interact in complex ways. The challenge is not only to estimate uncertainty but also to determine what kind of uncertainty is useful: epistemic uncertainty, aleatoric uncertainty, calibration under distribution shift, or the ability to detect out-of-distribution chemistry.

Uncertainty Quantification on CogSys Project Descriptions

Uncertainty Estimation in Atomistic Machine Learning: Calibration, Robustness, and Detection of Distribution Shift

Background