Bounding Retraining Equivalence and the Deletion Floor in Materials Machine Unlearning

In materials machine learning, closely related retained structures can support accurate property predictions even after a specific record is removed. Post-deletion prediction error is therefore an ambiguous measure of machine unlearning. We define the deletion floor as the expected loss on a deletion request under a specified retraining procedure. Standard indistinguishability constraints give a sharp interval bounding an unlearning update’s target loss around this reference. A conditional neighbor bound links a low deletion floor to retained-data fit, prediction regularity, and local label agreement; an exact ridge identity separates residual fit from the prediction change caused by deletion. In controlled redundancy sweeps, retaining just one related structure reduces median normalized retraining loss by approximately eightfold. In a paired Materials Project study spanning two fitting regimes, the regime with the lower deletion floor also shows a larger prediction change on more than 50% of shared requests. Comparisons with approximate updates and the original model distinguish deliberate suppression of target predictions from preservation of overall utility. Request-level unlearning evaluations should therefore report retraining reference loss, prediction change, and retained utility together, interpreting post-deletion accuracy in light of what retraining itself leaves behind.