Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Abstract Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key oppor...

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Bibliographic Details
Main Authors: Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, Mouhamad Diallo, Haegyeom Kim, Gerbrand Ceder
Format: Article
Language:English
Published: Nature Portfolio 2023-03-01
Series:npj Computational Materials
Online Access:https://doi.org/10.1038/s41524-023-00984-y

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https://doi.org/10.1038/s41524-023-00984-y

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