Automated Experiments of Local Non‐Linear Behavior in Ferroelectric Materials. Issue 48 (17th October 2022)

• Record Type: Journal Article
• Authors: Liu, Yongtao; Kelley, Kyle P.; Vasudevan, Rama K.; Zhu, Wanlin; Hayden, John; Maria, Jon‐Paul; Funakubo, Hiroshi; Ziatdinov, Maxim A.; Trolier‐McKinstry, Susan; Kalinin, Sergei V.
• Is Part Of: Small. Volume 18:Issue 48(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-17
• Subjects: automated experiments -- ferroelectrics -- machine learning -- non‐linearity -- piezoresponse force microscopy; Nanotechnology -- Periodicals; Nanoparticles -- Periodicals; Microtechnology -- Periodicals; 620.5
• DOI: 10.1002/smll.202204130
• Languages: English
• ISSNs: 1613-6810

Abstract

Abstract: An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function is developed and implemented. Here, the emergence of non‐linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non‐linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip‐surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non‐linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al0.93 B0.07 N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al0.93 B0.07 N, well‐poled regions show high linear piezoelectric responses, when paired with low non‐linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non‐linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. This technique can also be extended to electron, probe, and chemical imaging. Abstract : This work introduces an automated experiment for probing the origins of non‐linear behavior in ferroelectric materials, formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses to establish the preponderant physical mechanisms behind non‐linear behaviors. The approach is general and can be applied to structure‐property relationships via multimodal scanning probe, electron, and chemical imaging.