Machine learning reveals memory of the parent phases in ferroelectric relaxors Ba(Ti${}_{1-x}$,Zr${}_x$)O${}_3$

Machine learning has been establishing its potential in multiple areas of condensed matter physics and materials science. Here we develop and use an unsupervised machine learning workflow within a framework of first-principles-based atomistic simulations to investigate phases, phase transitions, and their structural origins in ferroelectric relaxors, Ba(Ti${}_{1-x}$,Zr${}_x$)O${}_3$. We first demonstrate the applicability of the workflow to identify phases and phase transitions in the parent compound, a prototypical ferroelectric BaTiO${}_3$. We then apply the workflow on Ba(Ti${}_{1-x}$,Zr${}_x$)O${}_3$, with $x\le 0.25$ to reveal (i) that some of the compounds bear a subtle memory of BaTiO${}_3$, 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 $x$