Machine Learning Reveals Memory of the Parent Phases in Ferroelectric Relaxors Ba(Ti1−x_1-x, Zrx)O3. Issue 3 (22nd January 2023) - Ladera, Adriana; Kashikar, Ravi; Lisenkov, S.; Ponomareva, I.
Abstract: Machine 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 1 − x _1-x, Zr x )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 1 − x _1-x, Zr x )O3 with x ≤ 0.25 x0.25 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 . Abstract : The work proposes an unsupervised machine learning (ML) workflow that utilizes principal component analysis and K‐means clustering to study the relaxor ferroelectric Ba(Ti 1 − x _1-x, Zr x )O3 and its parent compound, BaTiO3 . The ML workflow correctly identifies phase transitions in BaTiO3 and detects subtle phases in Ba(Ti 1 − x _1-x, Zr x )O3 which are overlooked by the traditional thermodynamic approach.