Towards Extended Active Learning for Modelling Ferroelectric Switching: the Need for 'Gold Standards'
Abstract
For the purpose of modelling ferroelectric switching in wurtzite-structured materials, four cost-effective density-functional theory (DFT) methods (PBE, PBEsol, r2SCAN, and r2SCAN-rVV10) are considered and compared to various ab initio approaches based on the random-phase approximation (RPA), including RPA with singles corrections (RPAR+S), as well as second-order Møller-Plesset perturbation theory (MP2). The purpose is to determine whether an ab initio approach could act as a 'gold standard' for estimating the reliability of DFT, thus determining an optimal DFT method for use in exhaustive tasks such as the training of machine-learning interatomic potentials (MLIP) for large-scale simulations of materials of arbitrary composition and structure such as Al1-xScxN and Zn1-xMgxO, using AlN, Al0.5Sc0.5N, ZnO, and Zn0.5Mg0.5O as model materials. Applications of active learning (AL) are now common, in which results from MLIP simulations are used to enhance the DFT training data set, but future extended active learning (EAL) methods will also need to systematically assess the DFT methodology against a gold standard. Herein, the variability of the ab initio results is found to exceed that required for a robust gold standard, but the DFT and ab initio approaches appear to converge on RPAR+S and r2SCAN-rVV10 as optimal method choices to initiate EAL. The electron correlation energy is found to be dominated by covalent binding effects associated with the high-electron-density anions involved, but the van der Waals dispersion force is seen to be too significant to ignore in ferroelectric modelling.
Related
- When Screening Current Controls Ferroelectric Switching: From Field-Limited to Current-Limited Regimes
- Orientation engineering is a universal strategy for ferroelectric trans-switching
- Light-facilitated ferroelectric switching in wurtzite crystals
- Ferroelectric Switching in ZnO/Zn1-xMgxO Heterostructures: Atomistic Insights into Interfacial Coupling and Layer Architecture
- Ferroelectric switching of odd-parity magnon spin splitting
BibTeX
@misc{reimers2026towards,
title = {{Towards Extended Active Learning for Modelling Ferroelectric Switching: the Need for 'Gold Standards'}},
author = {Jeffrey R. Reimers and Wenbin Zhang and Zhe Su and Musen Li and Carla Verdi and Tim Gould and Wei Ren},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.27108},
eprint = {2609.27108},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.27108},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Towards Extended Active Learning for Modelling Ferroelectric Switching: the Need for 'Gold Standards'
AU - Jeffrey R. Reimers
AU - Wenbin Zhang
AU - Zhe Su
AU - Musen Li
AU - Carla Verdi
AU - Tim Gould
AU - Wei Ren
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.27108
UR - https://arxiv.org/abs/2609.27108
AB - For the purpose of modelling ferroelectric switching in wurtzite-structured materials, four cost-effective density-functional theory (DFT) methods (PBE, PBEsol, r2SCAN, and r2SCAN-rVV10) are considered and compared to various ab initio approaches based on the random-phase approximation (RPA), including RPA with singles corrections (RPAR+S), as well as second-order Møller-Plesset perturbation theory (MP2). The purpose is to determine whether an ab initio approach could act as a 'gold standard' for estimating the reliability of DFT, thus determining an optimal DFT method for use in exhaustive tasks such as the training of machine-learning interatomic potentials (MLIP) for large-scale simulations of materials of arbitrary composition and structure such as Al1-xScxN and Zn1-xMgxO, using AlN, Al0.5Sc0.5N, ZnO, and Zn0.5Mg0.5O as model materials. Applications of active learning (AL) are now common, in which results from MLIP simulations are used to enhance the DFT training data set, but future extended active learning (EAL) methods will also need to systematically assess the DFT methodology against a gold standard. Herein, the variability of the ab initio results is found to exceed that required for a robust gold standard, but the DFT and ab initio approaches appear to converge on RPAR+S and r2SCAN-rVV10 as optimal method choices to initiate EAL. The electron correlation energy is found to be dominated by covalent binding effects associated with the high-electron-density anions involved, but the van der Waals dispersion force is seen to be too significant to ignore in ferroelectric modelling.
ER -