Latent Genetic Algorithm for Crystal Structure Prediction
Predicting crystal structures requires navigating rugged energy landscapes in which favorable local motifs must be inherited across candidates with incompatible cells, densities, and symmetries. Conventional real-space crossover often destroys these motifs when parent structures are geometrically mismatched. Here we show that latent representations learned by pretrained universal interatomic potentials can serve as continuous evolutionary coordinates for crystal structure prediction. In the Latent Genetic Algorithm (LGA), offspring are generated by inverse optimization of atomic positions and lattice vectors to match a target latent representation, which is constructed via interpolation of the parent latent vectors. LGA suppresses high-energy and short-contact offspring, increases the HfO$_2$ ground-state recovery rate from 20-35% to 60-95%, and enables a unified variable-supercell search over 16 perovskites with a nearly tenfold reduction in search cost. Applied to (PbTiO$_3$)$_n$/(PbZrO$_3$)$_n$ superlattices, LGA reveals $\sqrt{2} \times 3\sqrt{2} \times 1$ long-period ground-state structures characterized by a common in-plane finite-$q$ modulation $q{_\parallel} = (1/6,1/6)$ and layer-coupled sidebands. To our knowledge, this in-plane periodicity has not been reported in any related oxide perovskite superlattice studies. Altogether, LGA offers a powerful representation-guided paradigm for ground-state structure prediction and provides a practical, decoder-free route toward materials inverse design.