Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data

Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data

To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and computed data must be established. In materials development, X-ray diffraction (XRD) is an effective technique for analysing c...

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Bibliographic Details
Main Authors: Ryo Murakami, Taisuke T. Sasaki, Hideki Yoshikawa, Yoshitaka Matsushita, Keitaro Sodeyama, Tadakatsu Ohkubo, Hiroshi Shinotsuka, Kenji Nagata
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Science and Technology of Advanced Materials: Methods
Subjects:
Materials informatics
spectral decomposition
Bayesian estimation
feature selection
aI-ready
Online Access:https://www.tandfonline.com/doi/10.1080/27660400.2024.2384352
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https://www.tandfonline.com/doi/10.1080/27660400.2024.2384352

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