Band Excitation Piezoresponse Force Microscopy of PbZr_0.2Ti_0.8O₃

Film grown and measurements conducted by Joshua C. Agar at Oak Ridge National Laboratory This dataset has been the subject of 4 manuscripts: 1. Agar, J., Damodaran, A. R., Pandya, S., C, Cao, Y., Vasudevan, R. K., Xu, R., Saremi, S., Li, Q., Kim, J., McCarter, M. R., Dedon, L. R., Angsten, T., Balke, N., Jesse, S., Asta, M., Kalinin, S. V. & Martin, L. W. Three-State Ferroelastic Switching and Large Electromechanical Responses in PbTiO<sub>3 Thin Films. Adv. Mater. 29, 1702069 (2017). [doi:10.1002/adma.201702069](https://onlinelibrary.wiley.com/doi/10.1002/adma.201702069) 2. Agar, J. C., Cao, Y., Naul, B., Pandya, S., van der Walt, S., Luo, A. I., Maher, J. T., Balke, N., Jesse, S., Kalinin, S. V., Vasudevan, R. K. & Martin, L. W. Machine detection of enhanced electromechanical energy conversion in PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub> thin films. Adv. Mater. 30, e1800701 (2018). [doi:10.1002/adma.201800701](https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.201800701) 3. Griffin, L. A., Gaponenko, I. & Bassiri-Gharb, N. Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films. Adv. Mater. e2002425 (2020). [doi:10.1002/adma.202002425](https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202002425) 4. Qin, S., Guo, Y., Kaliyev, A. T. & Agar, J. C. Why it is Unfortunate that Linear Machine Learning ‘Works’ so well in Electromechanical Switching of Ferroelectric Thin Films. Adv. Mater. e2202814 (2022). [doi:10.1002/adma.202202814](https://onlinelibrary.wiley.com/doi/10.1002/adma.202202814)