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Automated crystal structure analysis based on blackbox optimisation

Yoshihiko Ozaki, Yuta Suzuki, Takafumi Hawai, Kotaro Saito, Masaki Onishi, Kanta Ono

2020npj Computational Materials55 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

AutomationComputer scienceVariance (accounting)Process (computing)Bayesian probabilityData miningHuman errorQuality (philosophy)Artificial intelligenceMachine learningStatisticsMathematicsEngineeringPhilosophyOperating systemEpistemologyAccountingBusinessMechanical engineeringMachine Learning in Materials ScienceX-ray Diffraction in CrystallographyComputational Drug Discovery Methods
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