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Bayesian inference of metal oxide ultrathin film structure based on crystal truncation rod measurements
Author(s) -
Anada Masato,
Nakanishi-Ohno Yoshinori,
Okada Masato,
Kimura Tsuyoshi,
Wakabayashi Yusuke
Publication year - 2017
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s1600576717013292
Subject(s) - monte carlo method , bayesian inference , software , truncation (statistics) , oxide , bayesian probability , computer science , algorithm , simulated annealing , materials science , statistical physics , physics , mathematics , artificial intelligence , machine learning , statistics , metallurgy , programming language
Monte Carlo (MC)‐based refinement software to analyze the atomic arrangements of perovskite oxide ultrathin films from the crystal truncation rod intensity is developed on the basis of Bayesian inference. The advantages of the MC approach are (i) it is applicable to multi‐domain structures, (ii) it provides the posterior probability of structures through Bayes' theorem, which allows one to evaluate the uncertainty of estimated structural parameters, and (iii) one can involve any information provided by other experiments and theories. The simulated annealing procedure efficiently searches for the optimum model owing to its stochastic updates, regardless of the initial values, without being trapped by local optima. The performance of the software is examined with a five‐unit‐cell‐thick LaAlO 3 film fabricated on top of SrTiO 3 . The software successfully found the global optima from an initial model prepared by a small grid search calculation. The standard deviations of the atomic positions derived from a dataset taken at a second‐generation synchrotron are ±0.02 Å for metal sites and ±0.03 Å for oxygen sites.