Open Access

ARTICLE

Cuckoo Search-Deep Neural Network Hybrid Model for Uncertainty Quantification and Optimization of Dielectric Energy Storage in Na_1/2Bi_1/2TiO₃-Based Ceramic Capacitors

Shige Wang¹, Yalong Liang², Lian Huang³, Pei Li^4,*

1 College of Architecture and Civil Engineering, Xinyang Normal University, Xinyang, 464000, China
2 Henan International Joint Laboratory of Structural Mechanics and Computational Simulation, College of Architectural and Civil Engineering, Huanghuai University, Zhumadian, 463000, China
3 Solux College of Architecture and Design, University of South China, Hengyang, 421000, China
4 Centre for Industrial Mechanics, Institute of Mechanical and Electrical Engineering, University of Southern Denmark, Sønderborg, 6400, Denmark

* Corresponding Author: Pei Li. Email:

(This article belongs to the Special Issue: Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials: Bridging Theory and Practice)

Computers, Materials & Continua 2025, 85(2), 2729-2748. https://doi.org/10.32604/cmc.2025.068351

Received 26 May 2025; Accepted 25 August 2025; Issue published 23 September 2025

Abstract

This study introduces a hybrid Cuckoo Search-Deep Neural Network (CS-DNN) model for uncertainty quantification and composition optimization of Na_1/2Bi_1/2TiO₃ (NBT)-based dielectric energy storage ceramics. Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate (1 − x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD, SEM, and electrical characterization. The key innovation lies in integrating the CS metaheuristic algorithm with a DNN, overcoming local minima in training and establishing a robust composition-property prediction framework. Our model accurately predicts room-temperature dielectric constant (ε_r), maximum dielectric constant (ε_max), dielectric loss (tan δ), discharge energy density (W_rec), and charge-discharge efficiency (η) from compositional inputs. A Monte Carlo-based uncertainty quantification framework, combined with the 3σ statistical criterion, demonstrates that CS-DNN outperforms conventional DNN models in three critical aspects: Higher prediction accuracy (R² = 0.9717 vs. 0.9382 for ε_max); Tighter error distribution, satisfying the 99.7% confidence interval under the 3σ principle; Enhanced robustness, maintaining stable predictions across a 25% composition span in generalization tests. While the model’s generalization is constrained by both the limited experimental dataset (n = 45) and the underlying assumptions of MC-based data augmentation, the CS-DNN framework establishes a machine learning-guided paradigm for accelerated discovery of high-temperature dielectric capacitors through its unique capability in quantifying composition-level energy storage uncertainties.

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Keywords

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