| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Machine Learning Ferroelectrics: Bayesianity, Parsimony, and Causality |
| Author(s) |
Sergei Kalinin |
| On-Site Speaker (Planned) |
Sergei Kalinin |
| Abstract Scope |
Scanning Transmission Electron Microscopy and Piezoresponse Force Microscopy has opened a window into atomic and mesoscale functionalities of ferroelectric materials. However, this wealth of data necessitates development of pathways to extract the generative physics, either in the form of parameters of mesoscopic Ginzburg-Landau model, or corresponding atomistic descriptors. One such approach is based on the Bayesian methods that allow to take into consideration the prior knowledge the system and evaluate the changes in understanding of the behaviors given new data. The second pathway explores the parsimony of physical laws and aims to extract these from the set of real-world observations. Ultimately, we seek to answer the questions such as whether frozen atomic disorder drives the emergence of the local structural distortions or polarization field instability drives cation and oxygen vacancy segregation, and what is the driving forces controlling the emergence of unique functionalities of morphotropic materials and ferroelectric relaxors. |