Duplicated-Graph Training for Machine Learning Force Fields

Machine learning force fields now approach quantum-mechanical accuracy at a small fraction of its cost, yet every training label still requires an electronic-structure calculation, so how much a model learns from each label determines how far it reaches. Nearly all such models share one interface to their data: a labeled conformation is converted into a single cutoff graph, and that graph is everything the network ever sees of it. We revisit that interface with duplicated-graph training, which pairs each labeled conformation with a rigidly translated copy of itself and applies one cutoff rule to both, so that atoms interact within each copy and across the two. Rigidity leaves every intramolecular distance and angle intact, so the reference energy and forces remain exact labels, while the geometry between the copies varies with the translation. Resampling the translation therefore lets a single label constrain the model on a family of interaction geometries, with no additional electronic-structure calculations and no change to the architecture. Applied to MACE, duplicated graphs lower force error on every system we study: by 16.4% on average across the ten rMD17 molecules and by 9 to 20% on the seven MD22 systems. The gains are largest where the test conformations depart from the training distribution, growing on 3BPA from 4.57% at 300 K to 16.55% at 1,200 K. Distillation transfers the improvement to a model that runs on an ordinary graph at unchanged inference cost, retaining a 15.6% average force reduction on rMD17.