Automated prediction of lattice parameters from X-ray powder diffraction patterns.

Autor: Chitturi SR; Materials Science and Engineering, Stanford University, Stanford, CA94305, USA.; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA., Ratner D; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA., Walroth RC; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA., Thampy V; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA., Reed EJ; Materials Science and Engineering, Stanford University, Stanford, CA94305, USA., Dunne M; Materials Science and Engineering, Stanford University, Stanford, CA94305, USA.; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA., Tassone CJ; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA., Stone KH; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.
Jazyk: angličtina
Zdroj: Journal of applied crystallography [J Appl Crystallogr] 2021 Nov 30; Vol. 54 (Pt 6), pp. 1799-1810. Date of Electronic Publication: 2021 Nov 30 (Print Publication: 2021).
DOI: 10.1107/S1600576721010840
Abstrakt: A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.
(© Sathya R. Chitturi et al. 2021.)
Databáze: MEDLINE
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