Xinhui Gao,Yaqi Wang,Yongcheng Zhang,Shuzhi Sam Ge,Xiangxia Wei
High-performance lead-based relaxor-ferroelectric Pb(Mg 1/3 Nb 2/3 )O 3 -PbTiO 3 (PMN-PT) piezoceramics constitute a critical class of functional materials for advanced electromechanical transduction. However, achieving further performance enhancement is fundamentally hindered by the complexity of compositional optimization, particularly the choice of rare-earth dopant species and their precise concentrations. Machine learning (ML) has emerged as a transformative tool for navigating these high-dimensional compositional spaces and decoding complex physical origins. Herein, an interpretable and uncertainty-informed ML framework is developed to systematically investigate the piezoelectric coefficient ( d 33 ) of rare-earth-doped PMN-PT ceramics. By integrating ensemble regression with a multidimensional interpretability scheme, the framework explicitly decodes the underlying physical mechanisms, identifying the key factors governing d 33 variation, including rare-earth concentration, local structural disorder, B-site electronegativity, and lattice geometry. Guided by these insights, Sm and Eu are identified as the most promising dopants, with the highest predicted d 33 occurring near 2.5 mol% substitution. Predictive uncertainty remains well-controlled within the high-performance compositional domain near the morphotropic phase boundary (MPB), where the localized variance reflects the intrinsic structural instability and strong phase competition. This work establishes an interpretable and reliability-aware framework for deciphering complex solid solutions, bridging the gap between atomic-scale descriptors and macroscopic electromechanical properties.
Transparent piezoelectric ceramics, due to their ability to possess both piezoelectricity and light transmittance, have extensive applications in devices such as transparent sensors and transparent transducers. Generally, constructing ferroelectric domains with strong ferroelectricity and long-range order can yield a large piezoelectric coefficient. However, ferroelectric domain structures cause light scattering at the domain walls, thereby reducing the material's optical transmittance. Consequently, achieving both high optical transparency and a large piezoelectric performance simultaneously in piezoelectric ceramics presents a significant challenge. To address this challenge, La, Pr, Nd, Sm, Eu co-doped (1- x ) Pb(Mg 1/3 Nb 2/3 )O 3 - x PbTiO 3 (PMN-PT) transparent piezoelectric ceramics were prepared in this study based on a high-entropy strategy. Benefiting from the cocktail effect and lattice distortion effect of the high-entropy strategy, the largest piezoelectric performance d 33 of 1476 pC/N, along with a 60% linear transmittance and the highest linear transmittance up to 67% at 900 nm, along with high piezoelectric performance of 1334 pC/N were achieved. The influence of phase structure and ferroelectric domain structure on its optical, piezoelectric, dielectric, and ferroelectric properties was studied. The high-entropy transparent piezoelectric ceramics show enormous application potential in multifunctional photo-acoustic-electric coupling devices.
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