Abstract
In the present study, we show that time-consuming manual tuning of parameters in the Rietveld method, one of the most frequently used crystal structure analysis methods in materials science, can be automated by considering the entire trial-and-error process as a blackbox optimisation problem. The automation is successfully achieved using Bayesian optimisation, which outperforms both a human expert and an expert-system type automation despite the absence of expertise. This approach stabilises the analysis quality by eliminating human-origin variance and bias, and can be applied to various analysis methods in other areas which also suffer from similar tiresome and unsystematic manual tuning of extrinsic parameters and human-origin variance and bias.
Highlights
The physical properties of materials are governed by their crystal structure; crystal structure analysis is an indispensable element in materials science research[1,2]
In this study, based on blackbox optimisation (BBO)[14], we offer a general automation framework for Rietveld refinement to alleviate these problems and allow practitioners to spend more time examining candidate structures proposed by the framework, and not searching for them in a vast parameter space
The definition of GOF is given as follows: Here, we introduce our "BBO-Rietveld” approach that automates the tuning of the refinement scope in the Rietveld method in the same manner as previously done in hyperparameter optimisation (HPO)
Summary
The physical properties of materials are governed by their crystal structure; crystal structure analysis is an indispensable element in materials science research[1,2]. Powder 3D Parametric[40] is a semi-automated software refinement, yOi bs be the i-the observed diffraction intensity, yCx;ailc be the diffraction intensity calculated using the physical model toolkit for Rietveld refinement based on user-provided configuration This software is designed for sequential analysis of a large number of data such as time-resolved measurement. Our task can be formalised as the following BBO problem: is suitable for solving problems with stochastic state transitions which are formulated as a Marcov decision process This method is minimise RwpðxÞ; famous for its great success in playing games like Go. While the reinforcement learning-based approach is conceptually interestsubject to cðxÞ ! To the best of our knowledge, no attempt has been made to apply the BBO approach to Rietveld refinement for automating the time-consuming trial-and-error process and search beyond the boundaries defined by the common sense of human experts. We obtain the best configuration x? 1⁄4 argminxRwpðxÞ, which achieves the best fit without manual process
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