Statistical learning reveals causal links in ferroelectric material

Scientific Achievement: Researchers have shown that scanning transmission electron microscopy (STEM) can be used to learn causal mechanisms – rather than mere correlations – of atomistic behavior in ferroelectric perovskite across ferroelectric–antiferroelectric phase transitions.

Significance and Impact: Mechanistic knowledge of cause-and-effect relationships can disentangle complex emergent functionalities in ferroelectrics and is necessary for materials discovery, design, and optimization.

DOI: 10.1038/s41524-020-00396-2