Scientific Achievement: Demonstrated that structural order parameters and local concentration-driven phase transitions can be described on atomic level from scanning transmission electron microscopy (STEM) data.
Significance and Impact: The machine learning approach developed can be used to explore physics of phase transitions and chemical transformations on a single atom level, establishing relationship between local chemical composition, and average electron concentration.
DOI: 10.1126/sciadv.abd5084
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