Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior in Defect-Rich PZT Films.
Fengyuan ZhangKerisha N WilliamsDavid EdwardsAaron B NadenYulian YaoSabine M NeumayerAmit KumarBrian J RodriguezNazanin Bassiri-GharbPublished in: Small methods (2021)
Scanning Probe Microscopy (SPM) based techniques probe material properties over microscale regions with nanoscale resolution, ultimately resulting in investigation of mesoscale functionalities. Among SPM techniques, piezoresponse force microscopy (PFM) is a highly effective tool in exploring polarization switching in ferroelectric materials. However, its signal is also sensitive to sample-dependent electrostatic and chemo-electromechanical changes. Literature reports have often concentrated on the evaluation of the Off-field piezoresponse, compared to On-field piezoresponse, based on the latter's increased sensitivity to non-ferroelectric contributions. Using machine learning approaches incorporating both Off- and On-field piezoresponse response as well as Off-field resonance frequency to maximize information, switching piezoresponse in a defect-rich Pb(Zr,Ti)O 3 thin film is investigated. As expected, one major contributor to the piezoresponse is mostly ferroelectric, coupled with electrostatic phenomena during On-field measurements. A second component is electrostatic in nature, while a third component is likely due to a superposition of multiple non-ferroelectric processes. The proposed approach will enable deeper understanding of switching phenomena in weakly ferroelectric samples and materials with large chemo-electromechanical response.
Keyphrases
- single molecule
- machine learning
- high resolution
- atomic force microscopy
- systematic review
- molecular dynamics simulations
- living cells
- quantum dots
- high speed
- heavy metals
- high throughput
- healthcare
- optical coherence tomography
- squamous cell carcinoma
- health information
- deep learning
- cancer therapy
- fluorescent probe
- electronic health record
- mass spectrometry
- label free
- adverse drug
- drug induced