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 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.
- Authors:
-
- Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)
- OSTI Identifier:
- 1969980
- Grant/Contract Number:
- AC02-05CH11231; SC0012704; 1752814
- Resource Type:
- Accepted Manuscript
- Journal Name:
- npj Computational Materials
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 2057-3960
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Szymanski, Nathan J., Bartel, Christopher J., Zeng, Yan, Diallo, Mouhamad, Kim, Haegyeom, and Ceder, Gerbrand. Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification. United States: N. p., 2023.
Web. doi:10.1038/s41524-023-00984-y.
Szymanski, Nathan J., Bartel, Christopher J., Zeng, Yan, Diallo, Mouhamad, Kim, Haegyeom, & Ceder, Gerbrand. Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification. United States. https://doi.org/10.1038/s41524-023-00984-y
Szymanski, Nathan J., Bartel, Christopher J., Zeng, Yan, Diallo, Mouhamad, Kim, Haegyeom, and Ceder, Gerbrand. Thu .
"Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification". United States. https://doi.org/10.1038/s41524-023-00984-y. https://www.osti.gov/servlets/purl/1969980.
@article{osti_1969980,
title = {Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification},
author = {Szymanski, Nathan J. and Bartel, Christopher J. and Zeng, Yan and Diallo, Mouhamad and Kim, Haegyeom and Ceder, Gerbrand},
abstractNote = {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.},
doi = {10.1038/s41524-023-00984-y},
journal = {npj Computational Materials},
number = 1,
volume = 9,
place = {United States},
year = {Thu Mar 02 00:00:00 EST 2023},
month = {Thu Mar 02 00:00:00 EST 2023}
}
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