Force Alignment Does Not Predict Transfer: Contrasting Denoising and Force Regression

Recent approaches to pre-training atomistic neural networks refine the denoising objective to improve the implicitly learned force-field, assuming a direct link between force-field accuracy and downstream gains on property prediction. We test this premise with a matched comparison of denoising against supervised force regression, relating output and representation-level force alignment to fine-tuning performance. While denoising yields poorer and degrading force alignment, it still achieves decreasing downstream error and maintains an advantage over force regression. These results question whether explanations of downstream gains should be based on the accuracy of the learned force field.