PiezoTensorNet: Crystallography informed multi-scale hierarchical machine learning model for rapid piezoelectric performance finetuning

Sachin Poudel, Rubi Thapa, Rabin Basnet, Anna Timofiejczuk and Anil Kunwar

Applied Energy, 2024, vol. 361, issue C, No S0306261924002848

Abstract: Piezoelectric devices offer numerous opportunities for sustainable harvesting of wasted mechanical energy, leading to a significant interest in data-driven research on these materials. This study presents the design of PiezoTensorNet, a comprehensive framework that encompasses a hierarchical classification neural network for crystal point group determination and modular ensembles of regression-based multi-dimensional models for predicting piezoelectric tensors. The machine learning models capable of forecasting tensors for dopant element alloying and various crystallographic transformations is integrated along with finite element analysis for electromechanical performance evaluation. The efficacy of integrated toolkit is demonstrated through the computational design and discovery of a lead-free microelectromechanical system based on AlN. The introduction of Boron and Erbium dopants in AlN enhances its piezoelectric performance, particularly when the crystal undergoes rotations along a preferred axis. Specifically, under a vertical loading of 5 × 10−5 N/m2 applied to a cantilever beam, the preferentially oriented B0.3Er0.5Al0.2N material generates a power 9.96 times larger than that of AlN ceramics.

Keywords: Piezoelectric tensors; Crystal rotation; Energy conversion; Quasi-effective coefficients; Electromechanical finetuning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924002848
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002848

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.122901

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().