High-throughput computation of electric polarization in solids via Berry flux diagonalization

Abstract Electric polarization in the absence of an externally applied electric field is a key property of polar materials, but the standard interpolation-based ab initio approach to compute polarization differences within the modern theory of polarization presents challenges for automated high-throughput calculations. Berry flux diagonalization [J. Bonini et al., Phys. Rev. B 102 , 045141 (2020)] has been proposed as an efficient and reliable alternative, though it has yet to be widely deployed. Here, we assess Berry flux diagonalization using ab initio calculations of a large set of materials, introducing and validating heuristics that ensure branch alignment with a minimal number of intermediate interpolated structures. Our automated implementation of Berry flux diagonalization succeeds in cases where prior interpolation-based workflows fail due to band-gap closures or branch ambiguities. Benchmarking with ab initio calculations of 176 candidate ferroelectrics, we demonstrate the efficacy of the approach on a broad range of insulating materials and obtain accurate effective polarization values with fewer interpolated structures than prior automated interpolation-based workflows. Our real-space heuristics that can predict gauge stability a priori from ionic displacements enable a general automated framework for reliable polarization calculations and efficient high-throughput screening of chemically and structurally diverse polar insulators. These results establish Berry flux diagonalization as a robust and efficient method to compute the effective polarization of solids and to accelerate the data-driven discovery of functional polar materials.

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Publication Details

Journal
npj Computational Materials
Published
2026-01-20
DOI
https://doi.org/10.1038/s41524-025-01955-1
Primary Topic
Inorganic Chemistry and Materials
Type
article
Field-Weighted Citation Impact
0.00

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article

High-throughput computation of electric polarization in solids via Berry flux diagonalization

Abigail N. Poteshman, Francesco Ricci, Jeffrey B. Neaton
npj Computational Materials
Inorganic Chemistry and Materials
article

High-throughput computation of electric polarization in solids via Berry flux diagonalization

Abigail N. Poteshman, Francesco Ricci, Jeffrey B. Neaton
article en

Abstract

Abstract Electric polarization in the absence of an externally applied electric field is a key property of polar materials, but the standard interpolation-based ab initio approach to compute polarization differences within the modern theory of polarization presents challenges for automated high-throughput calculations. Berry flux diagonalization [J. Bonini et al., Phys. Rev. B 102 , 045141 (2020)] has been proposed as an efficient and reliable alternative, though it has yet to be widely deployed. Here, we assess Berry flux diagonalization using ab initio calculations of a large set of materials, introducing and validating heuristics that ensure branch alignment with a minimal number of intermediate interpolated structures. Our automated implementation of Berry flux diagonalization succeeds in cases where prior interpolation-based workflows fail due to band-gap closures or branch ambiguities. Benchmarking with ab initio calculations of 176 candidate ferroelectrics, we demonstrate the efficacy of the approach on a broad range of insulating materials and obtain accurate effective polarization values with fewer interpolated structures than prior automated interpolation-based workflows. Our real-space heuristics that can predict gauge stability a priori from ionic displacements enable a general automated framework for reliable polarization calculations and efficient high-throughput screening of chemically and structurally diverse polar insulators. These results establish Berry flux diagonalization as a robust and efficient method to compute the effective polarization of solids and to accelerate the data-driven discovery of functional polar materials.

npj Computational MaterialsVol. 12(1)
Argonne National Laboratory (US), Lawrence Berkeley National Laboratory (US), University of Chicago (US), Kavli Energy NanoScience Institute (US), Applied Mathematics (United States) (US), Grand Charleroi Hospital (BE), University of California, Berkeley (US), UCLouvain (BE)
U.S. Department of Energy, National Energy Research Scientific Computing Center, European Commission, Office of Science, Basic Energy Sciences, Lawrence Berkeley National Laboratory
Openalex Percentile: Top 84%
Inorganic Chemistry and Materials
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