Ghost Atoms: Flexible Crystal Generation With Voronoi Tessellations

Generative models for materials discovery fix the number of atoms per unit cell before generation, so the model never chooses how many atoms its crystal contains and cannot add or remove them. We restore this degree of freedom by padding every cell to a fixed number of atoms with fictitious ghost atoms, placed at well-spaced interstitial sites. We place them by iterative Voronoi maximin selection, so the padded training structures contain no unphysical atomic overlaps. Training the OMatG stochastic-interpolant model on an augmented MP-20 with Voronoi-placed ghosts improves the combined rate of stable or metastable, unique, and novel structures (SUN+MSUN) by 2.6 percentage points over a matched control that places ghosts uniformly at random, and by 6.0 points over the published OMatG model. The Voronoi-trained model learns to keep ghosts out of occupied space, placing far fewer near real atoms, so that converting a ghost into a real atom happens at a vacant site. Our best run reaches a 25.27% SUN+MSUN rate on LeMat-GenBench, a new state of the art.