Efficient fine‑tuning of foundation atomistic models for reversible polymorphic phase transitions in organic molecular crystals

Foundation atomistic models—pretrained, general-purpose machine learning interatomic potentials—offer a promising path toward universal, data-efficient atomistic simulation across chemistry and materials science. However, their ability to capture rare, collective phenomena in complex molecular systems remains largely untested. Here, we systematically evaluate the transferability and fine-tuning of four state-of-the-art foundation atomistic models (MACE-MP-0b, MACE-OFF-small, SevenNet, and CHGNet) for simulating the reversible α⇌β polymorphic phase transition in the prototypical organic molecular crystal 2,4,5-triiodo-1H-imidazole (tIIm). Out-of-the-box, none of the models can reproduce the complete reversible transition, reflecting their limitations in capturing subtle and finely balanced non-covalent interactions outside their pretraining domain. By contrast, targeted fine-tuning—especially using our recently developed parameter-efficient equivariant low-rank adaptation (ELoRA) method—enables robust simulation of both transition directions and recovers the full collective dynamics of the phase change. Mechanistic analysis reveals a stepwise transition pathway with pronounced asymmetry in energy barriers, consistent with experimental observations. Our results establish practical guidelines for adapting foundation atomistic models to challenging materials phenomena and demonstrate that efficient fine-tuning protocols are essential for realizing the promise of universal, transferable machine learning interatomic potentials in molecular materials simulation.