General Learning of the Electric Response of Inorganic Materials

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Main Authors: Martin, Bradley A. A., Ganose, Alex M., Kapil, Venkat, Li, Tingwei, Butler, Keith T.
Format: Preprint
Published: 2025
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author Martin, Bradley A. A.
Ganose, Alex M.
Kapil, Venkat
Li, Tingwei
Butler, Keith T.
author_facet Martin, Bradley A. A.
Ganose, Alex M.
Kapil, Venkat
Li, Tingwei
Butler, Keith T.
contents We present MACE-Field, a field-aware $O(3)$-equivariant interatomic potential that provides a compact, derivative-consistent route to dielectric properties (such as polarisation $\mathbf P$, Born effective charges $Z^*$ and polarisability $\boldsymbolα$) and finite-field simulations across chemistry for inorganic solids. MACE-Field preserves the standard MACE readout and can inherit existing MACE foundation weights, turning pretrained models into field-aware ones with minimal change. To demonstrate, we fine-tune MACE-MP-0 on multiple heads covering BECs and polarisabilities ($\sim$6k MP dielectrics spanning 81 elements), polarisations (2.5k MP nonpolar-to-polar polarisation branches), and energies, forces, and stresses (10,000 structure-replay set from MPtraj), resulting in a field-aware foundation model, MACE-Field-MP-0. We show that MACE-Field can evaluate polarisation branches and spontaneous polarisations, predict $Z^*$ and dielectric constants across diverse chemistries, and reproduce finite-field MD simulations, such as BaTiO$_3$ polarisation hysteresis and the IR/Raman and dielectric spectra of $α$-quartz, benchmarking against Allegro-pol and DFPT.
format Preprint
id arxiv_https___arxiv_org_abs_2508_17870
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle General Learning of the Electric Response of Inorganic Materials
Martin, Bradley A. A.
Ganose, Alex M.
Kapil, Venkat
Li, Tingwei
Butler, Keith T.
Materials Science
We present MACE-Field, a field-aware $O(3)$-equivariant interatomic potential that provides a compact, derivative-consistent route to dielectric properties (such as polarisation $\mathbf P$, Born effective charges $Z^*$ and polarisability $\boldsymbolα$) and finite-field simulations across chemistry for inorganic solids. MACE-Field preserves the standard MACE readout and can inherit existing MACE foundation weights, turning pretrained models into field-aware ones with minimal change. To demonstrate, we fine-tune MACE-MP-0 on multiple heads covering BECs and polarisabilities ($\sim$6k MP dielectrics spanning 81 elements), polarisations (2.5k MP nonpolar-to-polar polarisation branches), and energies, forces, and stresses (10,000 structure-replay set from MPtraj), resulting in a field-aware foundation model, MACE-Field-MP-0. We show that MACE-Field can evaluate polarisation branches and spontaneous polarisations, predict $Z^*$ and dielectric constants across diverse chemistries, and reproduce finite-field MD simulations, such as BaTiO$_3$ polarisation hysteresis and the IR/Raman and dielectric spectra of $α$-quartz, benchmarking against Allegro-pol and DFPT.
title General Learning of the Electric Response of Inorganic Materials
topic Materials Science
url https://arxiv.org/abs/2508.17870