论文标题
机器学习揭示了铁电松弛剂BA中父阶段的记忆（ti $ _ {1-x} $，zr $ _x $）o $ _3 $
Machine learning reveals memory of the parent phases in ferroelectric relaxors Ba(Ti$_{1-x}$,Zr$_x$)O$_3$
论文作者
论文摘要
机器学习一直在凝结物理学和材料科学的多个领域中建立了潜力。在这里，我们在基于第一原理的原子模拟的框架内开发和使用无监督的机器学习工作流程，以研究铁电松弛剂中的阶段，相变及其结构性起源，BA（ti $ _ {1-x}} $，Zr $ _x $ _x $）O $ _3 $。我们首先证明工作流程的适用性，以识别父级化合物中的阶段和相变（一种原型的铁电batio $ _3 $。然后，我们将工作流程应用于ba（ti $ _ {1-x} $，zr $ _x $）o $ _3 $，并带有$ x \ leq0.25 $，以揭示（i），某些化合物对Batio $ _3 $的细微记忆具有微妙的记忆，超出了Pinch Edection Exprient的贡献，这些阶段可以贡献其贡献的措施，以贡献其增强功能，以增强其增强的电子机制; （ii）存在具有纳米域的离域前体的特殊相 - 可能是有争议的极性纳米区域的候选者； （iii）纳米域阶段，最大的$ x $浓度
Machine learning has been establishing its potential in multiple areas of condensed matter physics and materials science. Here we develop and use an unsupervised machine learning workflow within a framework of first-principles-based atomistic simulations to investigate phases, phase transitions, and their structural origins in ferroelectric relaxors, Ba(Ti$_{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 $x\leq0.25$ 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 $x$
