Machine learning lattice constants from ionic radii and electronegativities for cubic perovskite $$A_{2}XY_{6}$$ A 2
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ORIGINAL PAPER
Machine learning lattice constants from ionic radii and electronegativities for cubic perovskite A2 XY6 compounds Yun Zhang1 · Xiaojie Xu1 Received: 11 June 2020 / Accepted: 18 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Metal halide perovskites have attracted great attention in the past decade due to unique and tunable optical and electrical properties, which are promising candidates for various applications such as solar cells, light-emitting diodes, and laser cooling devices. For cubic perovskites, the lattice constant, a, representing the size of the unit cell, has a significant impact on the structural stability, bandgap structure, and thus materials performance. In this study, we develop the Gaussian process regression (GPR) model to shed light on the relationship among ionic radii, electronegativities, and lattice constants for cubic perovskite A2 XY6 compounds. A total of 79 samples with lattice constants ranging from 8.109 to 11.790 Å are examined. The model has a high degree of accuracy and stability, contributing to fast, robust, and low-cost estimations of lattice constants. Keywords Lattice constant · Halide · Perovskite · Semiconductor · Machine learning · Gaussian process regression
Introduction The perovskite structure is a huge crystal structure group that includes several subclasses of space groups, including cubic, orthorhombic, tetragonal, rhombohedral, monoclinic, and triclinic crystals. The diversity in the stoichiometry and structures allows these materials to exhibit a variety of unique properties for many applications, such as high-temperature superconductors (Schwartz et al. 2017; Zhang et al. 2016a, b, 2014; Song et al. 2012; Thieme et al. 2009; Jiang et al. 2019; Shen et al. 2019; Wang et al. 2019; Qiu et al. 2017; Yang et al. 2019), multiferroic materials (Cheong and Mostovoy 2007), magnetoresistors (Li et al. 2013, 2017; Wang et al. 2013, 2012), magnetocalorics, and topological insulators (Jin et al. 2013). Recently, metal halide perovskites received great attention in various areas, such as light emitting diodes, solar cells, water splitting, and laser cooling (Burschka et al. 2013; Xing et al. 2014; Tan et al. 2014; Luo et al. 2014; Ha et al. 2016). These materials have unique optical and electrical properties, including the high
* Yun Zhang [email protected] Xiaojie Xu [email protected] 1
North Carolina State University, Raleigh, NC 27695, USA
absorption coefficient, low trap-state density, long carrier diffusion length, panchromatic light absorption, and strong photoluminescence, which are highly sought in the field of optoelectronics (Stranks et al. 2013; Kazim et al. 2014; Yin et al. 2014; Steirer et al. 2016). Since the discovery of lead halide perovskite compounds and their applications as solar absorbers in photovoltaic devices (Kojima et al. 2009; Kim et al. 2012), device performance has reached 23.7% of power conversion efficiency (PCE) of perovskite solar cells (Kojima et al. 2009; National Renewable Energy Labor
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