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Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning
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    Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning
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    Journal of the American Chemical Society

    Cite this: J. Am. Chem. Soc. 2024, 146, 44, 30340–30348
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    https://doi.org/10.1021/jacs.4c10244
    Published September 19, 2024
    Copyright © 2024 American Chemical Society

    Abstract

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    Powder X-ray diffraction (PXRD) is a cornerstone technique in materials characterization. However, complete structure determination from PXRD patterns alone remains time-consuming and is often intractable, especially for novel materials. Current machine learning (ML) approaches to PXRD analysis predict only a subset of the total information that comprises a crystal structure. We developed a pioneering generative ML model designed to solve crystal structures from real-world experimental PXRD data. In addition to strong performance on simulated diffraction patterns, we demonstrate full structure solutions over a large set of experimental diffraction patterns. Benchmarking our model, we predicted the structure for 134 experimental patterns from the RRUFF database and thousands of simulated patterns from the Materials Project on which our model achieves state-of-the-art 42 and 67% match rate, respectively. Further, we applied our model to determine the unreported structures of materials such as NaCu2P2, Ca2MnTeO6, ZrGe6Ni6, LuOF, and HoNdV2O8 from the Powder Diffraction File database. We extended this methodology to new materials created in our lab at high pressure with previously unsolved structures and found the new binary compounds Rh3Bi, RuBi2, and KBi3. We expect that our model will open avenues toward materials discovery under conditions which preclude single crystal growth and toward automated materials discovery pipelines, opening the door to new domains of chemistry.

    Copyright © 2024 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jacs.4c10244.

    • Model testing and training details, additional synthetic details, crystallographic refinement procedures and parameters, discussion of false positive structure assignments, Rietveld refinements, solved structure-types unseen during training, cosine similarity between experimental PXRD and simulated ground truth PXRD, and refinement details (PDF)

    Accession Codes

    CSD 23750112375026 contain the supplementary crystallographic data for this paper. These data can be obtained free of charge via www.ccdc.cam.ac.uk/data_request/cif, or by emailing [email protected], or by contacting The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; fax: +44 1223 336033.

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

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    This article is cited by 1 publications.

    1. Stefano Racioppi, Alberto Otero-de-la-Roza, Samad Hajinazar, Eva Zurek. Powder X-ray diffraction assisted evolutionary algorithm for crystal structure prediction. Digital Discovery 2025, 4 (1) , 73-83. https://doi.org/10.1039/D4DD00269E

    Journal of the American Chemical Society

    Cite this: J. Am. Chem. Soc. 2024, 146, 44, 30340–30348
    Click to copy citationCitation copied!
    https://doi.org/10.1021/jacs.4c10244
    Published September 19, 2024
    Copyright © 2024 American Chemical Society

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