Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics
| Title: | Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics |
|---|---|
| Authors: | Jingjin He, Changxin Wang, Junjie Li, Chuanbao Liu, Dezhen Xue, Jiangli Cao, Yanjing Su, Lijie Qiao, Turab Lookman, Yang Bai |
| Source: | Journal of Advanced Ceramics, Vol 12, Iss 9, Pp 1793-1804 (2023) |
| Publisher Information: | Tsinghua University Press, 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Clay industries. Ceramics. Glass |
| Subject Terms: | machine learning (ml), dielectric temperature spectrum, ferroelectrics, phase transition information, Clay industries. Ceramics. Glass, TP785-869 |
| More Details: | In material science and engineering, obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult. In this work, we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system, i.e., (Ba1−x−yCaxSry)(Ti1−u−v−wZruSnvHfw)O3. The deep neural network model uses physical features as inputs and directly outputs the full spectrum, in addition to yielding the octahedral factor, Matyonov–Batsanov electronegativity, ratio of valence electron to nuclear charge, and core electron distance (Schubert) as four key descriptors. Owing to the physically meaningful features, our model exhibits better performance and generalization ability in the broader composition space of BaTiO3-based solid solutions. And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature. Furthermore, the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information. The prediction is also experimentally validated by typical samples of (Ba0.85Ca0.15)(Ti0.98–xZrxHf0.02)O3. This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2226-4108 2227-8508 |
| Relation: | https://www.sciopen.com/article/10.26599/JAC.2023.9220788; https://doaj.org/toc/2226-4108; https://doaj.org/toc/2227-8508 |
| DOI: | 10.26599/JAC.2023.9220788 |
| Access URL: | https://doaj.org/article/a2e000dd781041bf87f2c62b68f617e0 |
| Accession Number: | edsdoj.2e000dd781041bf87f2c62b68f617e0 |
| Database: | Directory of Open Access Journals |
| ISSN: | 22264108 22278508 |
|---|---|
| DOI: | 10.26599/JAC.2023.9220788 |
| Published in: | Journal of Advanced Ceramics |
| Language: | English |