We present Parameter Quantification Network(PQ-Net),a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems.The network is tested ag...We present Parameter Quantification Network(PQ-Net),a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems.The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO_(2)-ZrO_(2)/Al_(2)O_(3) catalytic material system consisting of ca.20,000 diffraction patterns.It is shown that the network predicts accurate scale factor,lattice parameter and crystallite size maps for all phases,which are comparable to those obtained through full profile analysis using the Rietveld method,also providing a reliable uncertainty measure on the results.The main advantage of PQNet is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments.展开更多
In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak...In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak shifting,broadening and splitting,leading to inaccurate physicochemical information,such as lattice parameters and crystallite sizes.Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness.It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic,eliminating the need for prior knowledge of the sample’s chemical composition.We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data,acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.展开更多
基金We would like to thank Marco di Michiel(ID15A,ESRF)and Jakub Drnec(ID31,ESRF)for preparing beamline instrumentation and setup and for their help with the experimental XRD-CT data acquisition.We acknowledge ESRF for beamtime.Finden acknowledges funding through the Innovate UK Analysis for Innovators(A4i)program(Project No:106107)A.M.B.acknowledges EPSRC(grants EP/R026815/1 and EP/S016481/1).
文摘We present Parameter Quantification Network(PQ-Net),a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems.The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO_(2)-ZrO_(2)/Al_(2)O_(3) catalytic material system consisting of ca.20,000 diffraction patterns.It is shown that the network predicts accurate scale factor,lattice parameter and crystallite size maps for all phases,which are comparable to those obtained through full profile analysis using the Rietveld method,also providing a reliable uncertainty measure on the results.The main advantage of PQNet is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments.
基金funding through the Innovate UK Analysis for Innovators(A4i)programme(Project No.106003)Parts of this research were carried out at PETRA III.A.V.acknowledges financial support from the Royal Society as a Royal Society Industry Fellow(IF\R2\222059).
文摘In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak shifting,broadening and splitting,leading to inaccurate physicochemical information,such as lattice parameters and crystallite sizes.Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness.It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic,eliminating the need for prior knowledge of the sample’s chemical composition.We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data,acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.
