Machine‐learning techniques are more and more often applied to the analysis of complex behaviors in materials research. Frequently used to identify fundamental behaviors within large and multidimensional datasets, these techniques are strictly based on mathematical models. Thus, without inherent physical or chemical meaning or constraints, they are prone to biased interpretation. The interpretability of machine‐learning results in materials science, specifically materials’ functionalities, can be vastly improved through physical insights and careful data handling. The use of techniques such as dimensional stacking can provide the much needed physical and chemical constraints, while proper understanding of the assumptions imposed by model parameters can help avoid overinterpretation. These concepts are illustrated by application to recently reported ferroelectric switching experiments in PbZr0.2Ti0.8O3 thin films. Through systematic analysis and introduction of physical constraints, it is argued that the behaviors present are not necessarily due to exotic mechanisms previously suggested, but rather well described by classical ferroelectric switching superimposed by non‐ferroelectric phenomena, such as electrochemical deformation, electrostatic interactions, and/or charge injection.

Despite remarkable advances in characterization techniques of functional materials yielding an ever growing amount of data, the interplay between the physical and chemical phenomena underpinning materials' functionalities is still often poorly understood. Dimensional reduction techniques have been used to tackle the challenge of understanding materials' behavior, leveraging the very large amount of data available. Here, we present a method for applying physical and chemical constraints to dimensional reduction analysis, through dimensional stacking. Compared to traditional, uncorrelated techniques, this approach enables a direct and simultaneous assessment of behaviors across all measurement parameters, through stacking of data along specific dimensions as required by physical or chemical correlations. The proposed method is applied to the nanoscale electromechanical relaxation response in (1 − x)PMN-xPT solid solutions, enabling a direct comparison of electric fieldand chemical composition-dependent contributors. A poling-like, and a relaxation-like behavior with a domain glass state are identified, and their evolution is tracked across the phase diagram. The proposed dimensional stacking technique, guided by the knowledge of the underlying physics of correlated systems, is valid for the analysis of any multidimensional dataset, opening a spectrum of possibilities for multidisciplinary use.npj Computational Materials (2019) 5:85 ; https://doi.

This work investigates the role of Mn-doping of ferroelectric lead zirconate titanate (PZT) thin films exposed to a range of ionizing radiation doses. PZT thin films were fabricated with both undoped and 4% Mn-doped compositions, and the functional response was compared both before and after exposure to gamma radiation doses up to 10 Mrad. A phenomenological model was applied to quantify defect interactions and compare trends in the degradation of the functional response. Mn-doped PZT samples demonstrate reduced magnitude of functional response in non-irradiated samples but exhibit vastly superior radiation tolerance of dielectric and ferroelectric properties across the range of gamma doses studied here. Strong MnZr/Ti″−VO·· defect dipoles pin domain walls, resulting in a lower initial functional response and mitigating the deleterious effects of irradiation on extrinsic contributions to the said response. Piezoelectric response trends as a function of radiation dose are highly nonlinear. The results of this work can be leveraged to engineer next-generation radiation-tolerant ferroelectric materials for applications where high levels of functional response stability are required, especially at elevated ionizing radiation dose.

Thin film ferroelectric capacitors of composition Pb(Zr0.52Ti0.48)O3 were exposed to Fe3+ radiation (1011 to 1013 ions/cm2), and the change in the defect structure was investigated by Doppler broadening positron annihilation spectroscopy and other characterization techniques. As the radiation fluence increases, a systematic drop of the S parameter of the positron annihilation photopeak is observed and attributed to an increase in the Zr- and Ti-site related vacancies relative to the Pb-sites. The results demonstrate that the radiation has a more significant influence on the Zr- and Ti-sites relative to the Pb-sites. It is also observed that the S parameter of the Mn-doped samples is higher than the undoped counterparts. Coupled with ferroelectricity measurements and X-ray diffraction, the results suggest that the Mn dopant modifies the initial structure of the material and leads to a different functional response.

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