arXiv:2608.14502 2026 Architecture 1 ideas extracted · analyzed Sep 1, 2026
The paper's transferable construction is to represent a difficult temperature- and pressure-dependent scalar quantity as a pretrained static predictor plus a smooth residual surface. This reduces target dynamic range while preserving the pretrained model's structural knowledge, and it makes all response quantities derivatives of one scalar output rather than independently predicted heads. In neural materials models, the most promising implementation is a frozen or lightly fine-tuned structural encoder with a low-capacity thermodynamic residual head, trained jointly on free energies and automatically differentiated thermodynamic responses. The approach is especially attractive for sparse, expensive labels because the residual isolates the finite-temperature correction that the pretrained potential does not know.
Attach a small temperature-pressure residual head to a pretrained structural encoder instead of relearning the full free-energy surface. Predict one scalar Gibbs free energy and obtain entropy, volume, and other thermodynamic responses by automatic differentiation, enforcing that all outputs derive from a common potential.