Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films.

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 PbZr0.2 Ti0.8 O3 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.