Atom-Resolved Machine Learning of Dielectric and Piezoelectric Response

Abstract

Predicting dielectric and piezoelectric responses in structurally complex materials requires large simulation cells, for which density-functional perturbation theory (DFPT) calculations scale as $\mathcal{O}(N^4)$ and become computationally prohibitive. However, machine-learning approaches have remained challenging, limited either by the large amounts of expensive DFPT data required for direct regression or by the cubic cost of reconstruction. We here propose a machine-learning framework that learns the ionic response as an atom-indexed field: DART (Direct Atom-Resolved response-Tensor learning) learns these fields directly and achieves high accuracy from small training sets, whereas LARS (Linear-scaling Atom-Resolved Response Solver) reconstructs them from learned microscopic ingredients through sparse linear solves and requires no DFPT labels for the ionic dielectric or piezoelectric tensors. Using DART, we extrapolate to 314 stackings of AlN/ScN superlattices absent from training and identify a high-response polar candidate, for which DFPT gives a laterally clamped piezoelectric strain coefficient $d_{33,f}=14.511$ pC/N and $k_t^2=20.46\%$, exceeding the ordered 1AlN/1ScN reference by 63% and 66%.