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Machine learning has been establishing its potential in multiple areas ofcondensed matter physics and materials science. Here we develop and use anunsupervised machine learning workflow within a framework offirst-principles-based atomistic simulations to investigate phases, phasetransitions, and their structural origins in ferroelectric relaxors,Ba(Ti$_{1-x}$,Zr$_x$)O$_3$. We first demonstrate the applicability of theworkflow to identify phases and phase transitions in the parent compound, aprototypical ferroelectric BaTiO$_3$. We then apply the workflow onBa(Ti$_{1-x}$,Zr$_x$)O$_3$, with $x\leq0.25$ to reveal (i) that some of thecompounds bear a subtle memory of BaTiO$_3$, phases beyond the point of thepinched phase transition, which could contribute to their enhancedelectromechanical response; (ii) the existence of peculiar phases withdelocalized precursors of nanodomains -- likely candidates for thecontroversial polar nanoregions; and (iii) nanodomain phases for the largestconcentrations of $x$

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