Determined the kinetics of solid-state reactions by dynamic observations one defect at a time.
Significance and Impact
This work enables understanding solid state reactions in 2D materials, a key step towards developing applications in electronic and quantum materials.
– Dynamic scanning transmission electron microscopy (STEM) visualizes the phase transformation process in a layered material at the atomic level.
– A robust machine learning method was developed to automatically convert STEM movies into atomic positions without any limitation on data volume.
– The unsupervised method allows building a library of point defects and further to quantitatively describe their dynamics and kinetics of transformation processes.