Scientific Achievement
A machine learning method enables greater understanding mechanisms of ferroelectric switching under an atomic force microscope tip.
Significance and Impact
Machine learning in conjunction with scanning probe techniques enable leads to an understanding of how electromechanical energy conversion proceeds.
Research Details
– Ferroelectrics are widely used in sensors, actuators and transducers, but precise details on the mechanisms of their transduction are often lacking.
– Here, an approach based on scanning probe microscopy is combined with machine learning to understand these energy conversion processes on the nanoscale.
– This leads to a understanding of the impact of domain geometry on the pathways of ferroelectric/elastic switching.
J. C. Agar, Y. Cao, B. Naul, S. Pandya, S. van der Walt, A. I. Luo, J. T. Maher, N. Balke, S. Jesse, S. V. Kalinin, R. K. Vasudevan. and L. W. Martin, "Machine detection of enhanced electromechanical energy conversion in PbZr0.2Ti0.8O3 thin films," Advanced Materials (2018). DOI: 10.1002/adma.201800701
