Berlin 2018 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 75: Topical Session (Symposium MM): Big Data in Materials Science - Managing and exploiting the raw material of the 21st century
MM 75.3: Vortrag
Freitag, 16. März 2018, 12:00–12:15, H 0107
New Tolerance Factor to Predict Perovskite Oxide and Halide Stability — •Christopher J. Bartel1, Christopher Sutton2, Bryan R. Goldsmith3, Runhai Ouyang2, Charles B. Musgrave1, Luca M. Ghiringhelli2, and Matthias Scheffler2 — 1University of Colorado Boulder, Boulder, USA — 2Fritz-Haber- Institut der Max-Planck-Gesellschaft, Berlin, Germany — 3University of Michigan, Ann Arbor, USA
Using the novel data analytics approach sure independence screening and sparsifying operator (SISSO) [1] an accurate one-dimensional tolerance factor (τ) is developed that correctly classifies 92% of compounds as perovskite or nonperovskite for an experimental dataset containing 576 ABX3 materials. Importantly, τ has nearly a uniform performance across the five anion subsets: oxides (92%), fluorides (92%), chlorides (90%), bromides (93%), iodides (91%). In comparison, the widely used Goldschmidt tolerance factor (t) achieves a maximum accuracy of only 74% for the same set of materials, with a significantly lower accuracy for chlorides (52%), bromides (56%), and iodides (33%) than for oxides (83%) and fluorides (85%).The accuracy of τ combined with its simplicity, a continuous function of only the oxidation state of the A-site cation and Shannon ionic radii, allows for new physical insights into the stability of the perovskite structure and the prediction of more than 1,000 new stable inorganic and hybrid organic-inorganic double perovskite halides. [1] R. Ouyang, et al., arXiv:171003319 (2017).