Navigating polymorph generation and distilled-potential development via entropy-symmetry landscapes for metal plasticity mechanisms

Artificial intelligence has advanced crystal design, yet unifying crystal structure prediction with thermodynamics-driven structure-property modelling remains challenging owing to divergent methodological foundations. Here we show that an integrated framework called PolymorphGen‑MLPKD, driven by physically meaningful entropy-symmetry landscapes, enables targeted generation of polymorphs and concurrent structure-property modelling. The framework captures phase behaviour across diverse crystal systems, generates paracrystalline structures, reveals graphite-diamond transition pathways, and produces knowledge‑distilled potentials that transfer cross‑scale accuracy with a 106‑fold speed enhancement while preserving high generalizability. We uncover data‑efficiency and coverage‑uniformity scaling laws that inform machine learning model training. The resulting distilled potential successfully compares twinning and dislocation‑mediated plasticity across materials and further resolves stress‑induced phase transitions in brittle iridium. By bridging the generation-property gap in crystal artificial intelligence, this work overcomes conventional accuracy-efficiency limitations and provides a streamlined foundation for high‑fidelity atomic simulations. Crystal prediction and thermodynamics are hard to unify. The entropy-symmetry framework PolymorphGen-MLPKD enables polymorph generation, reveals phase transitions, and yields distilled potentials with high accuracy and efficiency.