StrainPair: Learning Elastic Response in Pretrained Interatomic Potentials

Elastic moduli measure how stress changes under deformation. Yet interatomic potentials are typically fine-tuned to match stresses one configuration at a time. We find that these two objectives can diverge: an adapted potential can predict elastic moduli more accurately even as its common stress-offset error increases. Motivated by this distinction, we introduce StrainPair, which augments conventional fine-tuning with a simple paired loss that matches stress differences between deformations of the same material. The loss uses existing labels and leaves the potential architecture and inference procedure unchanged. In our evaluation, StrainPair reduces shear- and bulk-modulus errors by 20.00% and 17.52% over matched pointwise fine-tuning across 10,987 materials. Three-seed experiments on 224 external materials confirm the gains. Stronger pointwise supervision, cross-material pairing, and affine output calibration do not recover the same improvements. Instead, the gains accompany a reduction in deformation-response error, despite larger common stress offsets. Our analysis explains this separation: stress differences cancel shared offsets and constrain variation along deformation paths. These results identify response accuracy as a distinct target for adapting pretrained potentials. They also reveal a tradeoff: better elasticity does not necessarily preserve energy, force, and stress accuracy on other configurations.