Multi-Fidelity Training Reshapes Uncertainty Decomposition in Atomistic Models

The study examines how multi‑fidelity training—using abundant low‑fidelity data and scarce high‑fidelity labels—affects uncertainty decomposition in atomistic models. By training deep ensembles of mean‑variance MACE models on ANI‑1x, the authors compare high‑fidelity training from scratch with low‑fidelity pretraining followed by fine‑tuning, evaluating aleatoric, epistemic, and total uncertainty under unseen molecular systems and higher‑energy configurations. Results show that pretraining lowers prediction error and improves uncertainty‑based ranking, but does not produce well‑calibrated predictive variances, especially under energy extrapolation, and that fine‑tuning yields a more effective uncertainty‑based acquisition function.

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• fine-tuning