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

Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, Mouhamad Diallo, Haegyeom Kim, Gerbrand Ceder

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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 opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

Original languageEnglish (US)
Article number31
Journalnpj Computational Materials
Issue number1
StatePublished - Dec 2023
Externally publishedYes

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© 2023, The Author(s).


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