Machine Learning Reveals Memory of the Parent Phases in Ferroelectric Relaxors Ba(Ti1−x$_{1-x}$,Zrx)O3

Abstract

AbstractMachine learning has been establishing its potential in multiple areas of condensed matter physics and materials science. Here an unsupervised machine learning workflow is developed and used within a framework of first‐principles‐based atomistic simulations to investigate phases, phase transitions, and their structural origins in ferroelectric relaxors, Ba(Ti,Zrx)O3. The applicability of the workflow is first demonstrated to identify phases and phase transitions in the parent compound, a prototypical ferroelectric BaTiO3. Then the workflow is applied for Ba(Ti,Zrx)O3 with to reveal i) that some of the compounds bear a subtle memory of BaTiO3 phases beyond the point of the pinched phase transition, which could contribute to their enhanced electromechanical response; ii) the existence of peculiar phases with delocalized precursors of nanodomains—likely candidates for the controversial polar nanoregions; and iii) nanodomain phases for the largest concentrations of x.