Convolutional Neural Networks to Assist the Assessment of Lattice Parameters from X-ray Powder Diffraction-Reference-Cited by-同舟云学术

Convolutional Neural Networks to Assist the Assessment of Lattice Parameters from X-ray Powder Diffraction

Published:2023-08-30 Issue:36 Volume:127 Page:7655-7664
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Gómez-Peralta Juan Iván¹^ORCID,Bokhimi Xim²^ORCID,Quintana Patricia¹

Affiliation:

1. Laboratorio Nacional de Nano y Biomateriales, CINVESTAV-IPN, Antigua Carretera a Progreso km 6, A. P. 37, 97310 Mérida, Yucatán, Mexico

2. Instituto de Física, Universidad Nacional Autónoma de México, A. P. 20-364, 01000 Ciudad de México, DF, Mexico

Funder

Consejo Nacional de Ciencia y Tecnolog?a

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.3c03860

Reference55 articles.

1. Will, G. Powder Diffraction: The Rietveld Method and the Two Stage Method to Determine and Refine Crystal Structures from Powder Diffraction Data; Springer-Verlag, 2006; p 4.

2. Dinnebier, R. E.; Leineweber, A.; Evans, J. S. O. Rietveld Refinement. Practical Powder Diffraction Pattern Analysis using TOPAS; De Gruyter: Berlin, 2019; pp 13–14.

3. Unit-cell refinement from powder diffraction scans

4. Ab-initio structure determination of LiSbWO6 by X-ray powder diffraction

5. TREOR, a semi-exhaustive trial-and-error powder indexing program for all symmetries

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