Accelerated discovery of high-performance piezocatalyst in BaTiO3-based ceramics via machine learning. (15th June 2022) - He, Jingjin; Yu, Chengye; Hou, Yuxuan; Su, Xiaopo; Li, Junjie; Liu, Chuanbao; Xue, Dezhen; Cao, Jiangli; Su, Yanjing; Qiao, Lijie; Lookman, Turab; Bai, Yang
Abstract: 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−y Cax Sry )(Ti1−u−v−w Zru Snv Hfw )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 (Ba0.95 Ca0.05 )(Ti0.9 Sn0.1 )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. Graphical Abstract: We use machine learning to accelerate the discovery of ceramic particles prepared by conventional solid-state reaction method with high piezocatalytic performance in the multi-component ferroelectric system (Ba1−x−y Cax Sry )(Ti1−u−v−w Zru Snv Hfw )O3 . After establishing a regression model as a surrogate model, we combine the predicted values and the associated uncertainty of the model to guide the search for compositions with high d33 in the vast search space. When the d33 predictions are mapped in the phase diagram, the meaning of the composition-property relationship becomes very clear: a relatively large d33 within the MPB region with the rhombohedral phase to one side. Then we select the system (Ba0.95 Ca0.05 )(Ti0.9 Sn0.1 )O3 as it combines a large d33 with the fewest number of elements. Its sintered bulk ceramic exhibits a relatively high d33 of 620 pC/N and a piezocatalytic activity with a degradation rate of 24.5 × 10 −3 min −1 for the RhB dye solution. ga1 Highlights: Machine learning accelerated composition design towards superior piezocatalytic performance in BaTiO3 -based ceramic powders. Data mining reveals the highest d33 occurs within the phase boundary region, especially on the rhombohedral phase side. (Ba0.95 Ca0.05 )(Ti0.9 Sn0.1 )O3 ceramic exhibits a highest degradation rate of 24.5 × 10 −3 min −1 for RhB dye.