Ferroelectric response of Na0.9Li0.1NbO3 at room temperature

Isaza-Zapata, V.; Arias, Arnaldo González; Maya, C.E.; Martinez, William M.; Agudelo, A. C.; Alvarez, Bernardo Francisco Meléndez; Gómez, Andrés Ávila; Izquierdo, J.L.

doi:10.1088/1742-6596/850/1/012009

Cited by 1 publication

(1 citation statement)

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“…For ANbO 3 , we find that any known ternary cation mixture ,− presents the value of Δ H c within 50 meV/ion, lying within the stability criterion. The criterion of APbI 3 is consistent with the representative and composition-weighted radii (Figure d).…”

Section: Resultssupporting

confidence: 53%

Learn-and-Match Molecular Cations for Perovskites

et al. 2019

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Forecasting the structural stability of hybrid organic/inorganic compounds, where polyatomic molecules replace atoms, is a challenging task; the composition space is vast, and the reference structure for the organic molecules is ambiguously defined. In this work, we use a range of machine-learning algorithms, constructed from state-of-the-art density functional theory data, to conduct a systematic analysis on the likelihood of a given cation to be housed in the perovskite structure. In particular, we consider both ABC3 chalcogenide (I–V–VI3) and halide (I–II–VII3) perovskites. We find that the effective atomic radius and the number of lone pairs residing on the A-site cation are sufficient features to describe the perovskite phase stability. Thus, the presented machine-learning approach provides an efficient way to map the phase stability of the vast class of compounds, including situations where a cation mixture replaces a single A-site cation. This work demonstrates that advanced electronic structure theory combined with machine-learning analysis can provide an efficient strategy superior to the conventional trial-and-error approach in materials design.

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