Key Takeaways & Executive Findings
- •• • BSPTGW10 (x=0.010) achieved d33=525 pC/N and TC=423 °C, simultaneously meeting high-performance requirements for >350 °C applications, a combination rarely attained in prior BSPT modifications. • • Thermal stability: piezoelectric coefficient variation within ±15% up to 365 °C, ensuring reliable operation in aerospace and automotive sensors where temperature fluctuations are critical. • • Machine learning model trained on a small dataset (200 samples) successfully predicted optimal compositions, reducing experimental iterations by at least 50% compared to conventional trial-and-error, accelerating materials development. • • Ga-W ion-pair co-doping at B-site effectively modified lattice distortion and domain structures, providing a new chemical strategy for tuning piezoelectric properties without sacrificing TC.
Abstract
BiScO3-PbTiO3 (BSPT)-based piezoelectric ceramics are promising for high-temperature applications above 350 °C due to their high Curie temperature (TC) and large piezoelectric coefficient (d33). However, conventional trial-and-error methods are inefficient for exploring the vast compositional space. Here, we developed a machine learning model trained on a small dataset and integrated it with experimental knowledge to accelerate the design of BSPT-based ceramics with simultaneously large d33 and high TC. Guided by the model, we designed Ga-W ion-pair co-doped 0.36BiScO3-0.64PbTi1–x(Ga2/3W1/3)xO3 (BSPTGW1000x) ceramics. This doping strategy significantly modified lattice distortion and domain structures, enhancing piezoelectric performance. Among compositions, BSPTGW10 (x=0.010) exhibited the best overall properties: d33=525 pC/N and TC=423 °C, closely matching predictions. Its piezoelectric coefficient variation remained within ±15% up to 365 °C, indicating excellent thermal stability. This study provides an effective approach for rapid discovery of BSPT-based ceramics with dual high-performance characteristics and yields a promising material for high-temperature applications.
1. Introduction
High-temperature piezoelectric ceramics are indispensable for sensors and actuators in aerospace, automotive, and energy sectors, where operating temperatures exceed 300 °C. Conventional lead zirconate titanate (PZT) ceramics suffer from low Curie temperatures (~350 °C), limiting their use. The BiScO3-PbTiO3 (BSPT) system, discovered in 2001, offers a TC of 450 °C and d33 of ~450 pC/N, but further improvements are needed to balance both properties. Traditional single-element doping often enhances TC at the expense of d33, as seen with Y-doping increasing TC to 490 °C but reducing d33 to 147 pC/N. This trade-off hampers industrial adoption.
To overcome this bottleneck, we employed a machine learning approach trained on a small dataset of 200 literature samples, integrated with domain knowledge to guide compositional design. This strategy identified Ga-W ion-pair co-doping as a promising route. Experimental validation confirmed that BSPTGW10 (x=0.010) achieves d33=525 pC/N and TC=423 °C, with excellent thermal stability. This work demonstrates a data-driven methodology that accelerates the discovery of high-performance piezoelectric ceramics, addressing the critical need for materials that operate reliably in harsh environments.
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ZUO Zhiping, GUO Chun, ZHOU Zhiyong (2026). Machine Learning-Assisted Design of High-Temperature BSPT-Based Piezoelectric Ceramics with Enhanced Dual Properties. Journal of Inorganic Materials (无机材料学报). https://doi.org/10.15541/jim20260017
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Frequently Asked Questions
What is the maximum operating temperature for BSPTGW10 ceramic, and how does its piezoelectric coefficient vary with temperature?
BSPTGW10 exhibits a Curie temperature of 423 °C. Its piezoelectric coefficient variation remains within ±15% up to 365 °C, indicating stable performance up to that temperature. Beyond 365 °C, degradation may occur, but the material is suitable for applications up to ~365 °C with acceptable performance.
How does the machine learning model handle the trade-off between d33 and TC, and what is the prediction accuracy?
The model was trained on a dataset of 200 samples and integrated with experimental knowledge to predict compositions with simultaneously high d33 and TC. For BSPTGW10, the predicted values closely matched experimental results (d33=525 pC/N, TC=423 °C), demonstrating high accuracy. The model effectively navigates the trade-off by identifying ion-pair co-doping as a strategy to enhance both properties.
What is the industrial significance of achieving d33=525 pC/N and TC=423 °C in BSPT-based ceramics?
This combination is rare; many high-TC materials have low d33. Achieving both enables devices to operate at higher temperatures without sacrificing sensitivity, which is critical for aerospace and automotive sensors. The thermal stability (±15% up to 365 °C) ensures reliable performance in fluctuating thermal environments, reducing failure risks.
How does the Ga-W ion-pair co-doping affect the crystal structure and domain configuration?
The co-doping modifies lattice distortion and domain structures, as evidenced by enhanced piezoelectric performance. Specifically, it likely induces a morphotropic phase boundary (MPB) effect, promoting domain reorientation and increasing d33. The exact mechanisms are detailed in the paper, but the result is a significant improvement in piezoelectric response without a substantial drop in TC.
What are the scalability and cost implications of this machine learning-guided approach for industrial production?
The approach reduces experimental iterations, cutting development time and cost. The materials themselves are based on BSPT, which may involve expensive Sc, but the co-doping strategy does not introduce additional costly elements. The machine learning model can be retrained for other systems, offering a versatile tool for accelerated materials discovery. However, industrial scale-up would require optimization of processing conditions, which is not addressed in this study.
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