基于机器学习的高温 BSPT 基压电陶瓷双重性能协同增强

doi:10.15541/jim20260017

摘要/Abstract

摘要： BiScO₃- PbTiO₃ (BSPT)基压电陶瓷因为高居里温度(T_c)以及高压电系数(d₃₃),成为350 ℃以上高温压电应用领域的重要候选材料。然而,传统试错法难以在广泛的成分空间范围内实现高温压电陶瓷的快速设计。本研究基于小样本数据集构建了机器学习模型,并结合实验先验知识,来加速兼具高压电系数和高居里温度的BSPT基压电陶瓷的设计。利用该模型,设计并制备了Ga-W离子对共掺杂的0.36BiScO₃-0.64PbTi_1-_x(Ga_2/3W_2/3)_xO₃(BSPTGW1000x)压电陶瓷。结果表明,该掺杂策略明显调控了BSPT基压电陶瓷的晶格畸变和畴结构,从而提升了压电性能。其中,BSPTGW10(x=0.010)陶瓷表现出最好的综合性能: d₃₃=525 pC/N,T_C =423 ℃,与模型预测性能接近;且在365 ℃以下,d₃₃变化率保持±15%以内,表现出优异的热稳定性。本研究不仅为快速发现同时具备高压电性能和高居里温度的 BSPT 基陶瓷提供了有效途径,而且获得了一种适用于高温应用的有前景的压电陶瓷材料。

关键词: 机器学习, 钪酸铋钛酸铅, 高温压电陶瓷, 数据驱动设计, 离子对掺杂

Abstract: BiScO₃- PbTiO₃ (BSPT)-based piezoelectric ceramics have emerged as one of the most promising candidates for high-temperature piezoelectric applications above 350 ℃ due to their high Curie temperature (T_C) and large piezoelectric coefficient (d₃₃). However, the conventional trial-and-error approach is inefficient for the rapid design of high-temperature piezoelectric ceramics across a wide compositional space. In this work, we developed a machine learning model trained on a small dataset and integrated it ed with experimental knowledge, to accelerate the design of BSPT-based ceramics with simultaneously high piezoelectricity and high Curie temperature. Guided by the trained model, we designed Ga-W ion-pair co-doped 0.36BiScO₃-0.64PbTi_1-_x(Ga_2/3W_1/3)_xO₃ (BSPTGW1000x) ceramics. The results demonstrated that this doping strategy significantly modified the lattice distortion and domain structures of BSPT-based ceramics, leading to enhanced piezoelectric performance. Among the compositions, BSPTGW10 (x = 0.010) exhibited the best overall properties (d₃₃ = 525 pC/N, T_C = 423 ℃), which were in close agreement with the predicted values. Moreover, its piezoelectric coefficient remained within ±15% variation up to 365 ℃, indicating excellent thermal stability. This study not only provides an effective approach for the rapid discovery of BSPT-based ceramics with dual high-performance characteristics, but also yields a promising piezoelectric ceramic material suitable for high-temperature applications. The datasets in this article are listed in Science Data Bank: https://www.doi.org/10.57760/sciencedb.27980.

Key words: machine learning, BiScO₃-PbTiO₃, high-temperature piezoelectric materials, data-driven design, ion-pair co-doping

中图分类号:

TQ174

左致平, 郭春, 周志勇. 基于机器学习的高温 BSPT 基压电陶瓷双重性能协同增强[J]. 无机材料学报, DOI: 10.15541/jim20260017.

ZUO Zhiping, GUO Chun, ZHOU Zhiyong. Machine Learning-Assisted Design of High-Temperature BSPT-Based Piezoelectric Ceramics with Enhanced Dual Properties[J]. Journal of Inorganic Materials, DOI: 10.15541/jim20260017.

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