Accelerated discovery of high-performance piezocatalyst in BaTiO3-based ceramics via machine learning

The study of piezocatalysis has become an important topic in piezoelectric research, especially for addressing environmental issues. However, the reliance on nanofabrication seriously hinders investigating materials with complex compositional design for higher performance and large-scale application. In this work, we use a machine learning strategy to efficiently sample the vast compositional space for ceramic powders with excellent piezoelectric response that we expect to impact piezocatalytic performance. The ceramics, synthesized by solid state reaction methods, belong to the multi-component system (Ba1−x−yCaxSry)(Ti1−u−v−wZruSnvHfw)O3 with target property d33, the piezoelectric coefficient. The highest d33 tends to occur within the phase boundary region with coexisting rhombohedral, orthorhombic and tetragonal phases, especially on the rhombohedral phase side. We select (Ba0.95Ca0.05)(Ti0.9Sn0.1)O3 as it combines a relatively large d33 with the fewest number of elements. Its sintered ceramic exhibits a high d33 of 605 ± 14 pC/N, consistent with the machine learning prediction 633 ± 70 pC/N. The mechanically-ground ceramic powders have an excellent piezocatalytic activity with a degradation rate of (2.16 ± 0.28) × 10−2 min−1 for RhB dye solution, comparable to the performance of previously reported nanoparticles. Our work provides further insight into the nature of piezoelectricity in BaTiO3 based ceramics, and affords an effective strategy for searching for superior piezocatalysts suitable for large-scale applications.