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In material science and engineering, obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult. In this work, we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system, i.e., (Ba1−x−yCaxSry)(Ti1−u−v−wZruSnvHfw)O3. The deep neural network model uses physical features as inputs and directly outputs the full spectrum, in addition to yielding the octahedral factor, Matyonov–Batsanov electronegativity, ratio of valence electron to nuclear charge, and core electron distance (Schubert) as four key descriptors. Owing to the physically meaningful features, our model exhibits better performance and generalization ability in the broader composition space of BaTiO3-based solid solutions. And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature. Furthermore, the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information. The prediction is also experimentally validated by typical samples of (Ba0.85Ca0.15)(Ti0.98–xZrxHf0.02)O3. This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information.

Abstract Introducing polarization field of piezoelectric materials is an effective strategy to improve photocatalytic performance. In this study, a new type of BaTiO3/CuO heterostructure catalyst was designed and synthesized to achieve high piezo-photocatalytic activity through the synergy of heterojunction and piezoelectric effect. The BaTiO3/CuO heterostructure shows a significantly enhanced piezo-photocatalytic degradation efficiency of organic pollutants compared with the individual BaTiO3 nanowires (NWs) and CuO nanoparticles (NPs). Under the co-excitation of ultrasonic vibration and ultraviolet radiation, the optimal degradation reaction rate constant k of polarized BaTiO3/CuO heterostructure on methyl orange (MO) dye can reach 0.05 min− 1, which is 6.1 times of photocatalytic rate and 7 times of piezocatalytic rate. The BaTiO3/CuO heterostructure with remarkable piezo-photocatalytic behavior provides a promising strategy for the development of high-efficiency catalysts for wastewater purification, and it also helps understand the coupling mechanism between piezoelectric effect and photocatalysis.