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Deep neural network for the dielectric response of insulators

Author(s): Zhang, Linfeng; Chen, Mohan; Wu, Xifan; Wang, Han; E, Weinan; et al

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Abstract: We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the deep potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab initio simulation. The scheme is nonperturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.
Publication Date: 12-Jun-2020
Citation: Zhang, Linfeng, Chen, Mohan, Wu, Xifan, Wang, Han, E, Weinan, Car, Roberto. (Deep neural network for the dielectric response of insulators. Physical Review B, 102 (4), 10.1103/physrevb.102.041121
DOI: doi:10.1103/physrevb.102.041121
ISSN: 2469-9950
EISSN: 2469-9969
Language: en
Type of Material: Journal Article
Journal/Proceeding Title: Physical Review B
Version: Author's manuscript

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