A field-aware neural network potential computes polarization, Born charges, and polarizability by exact differentiation of a learned electric enthalpy, reproducing DFT-level dielectric and ferroelectric response across materials.
Charge-constrained Atomic Cluster Expansion
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abstract
The atomic cluster expansion (ACE) efficiently parameterizes complex energy surfaces of pure elements and alloys. Due to the local nature of the many-body basis, ACE is inherently local or semilocal for graph ACE. Here, we employ descriptor-constrained density functional theory for parameterizing ACE with charge or other degrees of freedom, thereby transfering the variational property of the density functional to ACE. The descriptors can be of scalar, vectorial or tensorial nature. From the simplest case of scalar atomic descriptors we directly obtain charge-dependent ACE with long-range electrostatic interactions between variable charges. We observe that the variational properties of the charges greatly help in training, avoiding the need for charge-constrained DFT calculations.
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cond-mat.mtrl-sci 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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General Learning of the Electric Response of Inorganic Materials
A field-aware neural network potential computes polarization, Born charges, and polarizability by exact differentiation of a learned electric enthalpy, reproducing DFT-level dielectric and ferroelectric response across materials.