General Learning of the Electric Response of Inorganic Materials

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.

Publication Details

Journal
ArXiv.org
Published
2025-08-25
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

General Learning of the Electric Response of Inorganic Materials

ArXiv.org
Geochemistry and Geologic Mapping
article

General Learning of the Electric Response of Inorganic Materials

article en

Abstract

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.

ArXiv.org
Openalex Percentile: Top 64%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

General Learning of the Electric Response of Inorganic Materials · ArXiv.org (2025) | TGRS Research Map | TGRS