Issue 10, 2021

A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds

Abstract

Deep learning (DL) models trained with synthetic XRD data have never accomplished a satisfactory quantitative XRD analysis for the exact prediction of a constituent-phase fraction in unknown multiphase inorganic compounds, although DL-based phase identification has been successful. Here, we report a novel data-driven XRD analysis protocol involving a convolutional neural network (CNN) for exact phase identification and other machine learning (ML) techniques for accurate phase-fraction prediction. A key concept behind this reliable, pragmatic protocol is training with a huge amount of cheap synthetic data and testing with a small amount of expensive real-world experimental data. The protocol was applied to a Li–La–Zr–O quaternary compositional system that involves 218 ICSD-registered inorganic compounds, some of which are known as solid electrolyte materials. Synthetic data-driven XRD analysis has achieved a test accuracy of 96.47% for phase identification and a mean square error (MSE) of 0.0018 and an R2 of 0.9685 for phase-fraction regression. Real-world data tests have led to a phase-identification accuracy of 91.11% and a phase-fraction regression MSE of 0.0024 with an R2 of 0.9587.

Graphical abstract: A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds

Supplementary files

Article information

Article type
Research Article
Submitted
22 Dec 2020
Accepted
17 Mar 2021
First published
18 Mar 2021

Inorg. Chem. Front., 2021,8, 2492-2504

A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds

J. Lee, W. B. Park, M. Kim, S. Pal Singh, M. Pyo and K. Sohn, Inorg. Chem. Front., 2021, 8, 2492 DOI: 10.1039/D0QI01513J

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