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arXiv · 2508.17870

General Learning of the Electric Response of Inorganic Materials

Bradley A. A. Martin·Alex M. Ganose·Venkat Kapil·Tingwei Li·Keith T. Butler
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Abstract

We introduce \texttt{MACE-Field}, a field-aware, $O(3)$-equivariant interatomic potential that learns a single electric enthalpy functional $\mathcal F(\{\mathbf R\},\mathbf E)$ and obtains $\mathbf P$, $Z^*$, and $\boldsymbol\alpha$ by exact differentiation. A uniform field couples to latent equivariant features inside the \texttt{MACE} backbone, while the scalar energy readout preserves Maxwell reciprocity, the acoustic sum rule, and crystal tensor symmetries by construction. Because this coupling is a plug-in on top of standard \texttt{MACE}, existing energy/force foundation models can be upgraded to become field-aware. Benchmarked against semilocal DFT/DFPT reference data, a directly trained cross-chemistry ferroelectric model reproduces the same-branch Berry-phase and spontaneous polarisations across diverse inorganic crystals. Starting from the multihead foundation model \texttt{mace-mp-mh-0} and its OMAT-PBE head, joint fine-tuning on dielectric, ferroelectric, and replay data yields \texttt{MACE-Field-MH-0} foundation models, which predict $Z^*$, $\boldsymbol\alpha$, derived dielectric constants, and cross-chemistry polarisation trends with fidelity that captures branch-resolved polarisation and spontaneous-polarisation, while retaining strong force-field accuracy. Further, single-material \texttt{MACE-Field} models and \texttt{MACE-Field-MH-0} reproduce \ce{BaTiO3} hysteresis loops and $\alpha$-quartz infrared, Raman, and dielectric spectra from finite-field molecular dynamics, comparable to DFPT. These results show that a simple, physics-informed field coupling can endow atomistic foundation models with transferable dielectric and ferroelectric response, while targeted single-material training remains advantageous for the most quantitative spectroscopic predictions.

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Bradley A. A. Martin, Alex M. Ganose, Venkat Kapil, Tingwei Li, Keith T. Butler. 2025-08-25. General Learning of the Electric Response of Inorganic Materials. https://doi.org/10.1103/b116-xy8k

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