Coherent Diffraction Imaging(CDI)is an experimental technique to image isolated structures by recording the scattered light.The sample density can be recovered from the scattered field through a Fourier Transform oper...Coherent Diffraction Imaging(CDI)is an experimental technique to image isolated structures by recording the scattered light.The sample density can be recovered from the scattered field through a Fourier Transform operation.However,the phase of the field is lost during the measurement and has to be algorithmically retrieved.Here we present SPRING,an analysis framework tailored to X-ray Free Electron Laser(XFEL)single-shot single-particle diffraction data that implements the Memetic Phase Retrieval method to mitigate the shortcomings of conventional algorithms.We benchmark the approach on data acquired in two experimental campaigns at SwissFEL and European XFEL.Results reveal unprecedented stability and resilience of the algorithm’s behavior on the input parameters,and the capability of identifying the solution in conditions hardly treatable with conventional methods.A user-friendly implementation of SPRING is released as open-source software,aiming at being a reference tool for the CDI community at XFEL and synchrotron facilities.展开更多
Single-shot coherent diffraction imaging of isolated nanosized particles has seen remarkable success in recent years,yielding in-situ measurements with ultra-high spatial and temporal resolution.The progress of high-r...Single-shot coherent diffraction imaging of isolated nanosized particles has seen remarkable success in recent years,yielding in-situ measurements with ultra-high spatial and temporal resolution.The progress of high-repetition-rate sources for intense X-ray pulses has further enabled recording datasets containing millions of diffraction images,which are needed for the structure determination of specimens with greater structural variety and dynamic experiments.The size of the datasets,however,represents a monumental problem for their analysis.Here,we present an automatized approach for finding semantic similarities in coherent diffraction images without relying on human expert labeling.By introducing the concept of projection learning,we extend self-supervised contrastive learning to the context of coherent diffraction imaging and achieve a dimensionality reduction producing semantically meaningful embeddings that align with physical intuition.The method yields substantial improvements compared to previous approaches,paving the way toward real-time and large-scale analysis of coherent diffraction experiments at X-ray free-electron lasers.展开更多
Correction to:npj Computational Materials https://doi.org/10.1038/s41524-023-00966-0,published online 16 February 2023 The original version of this Article contained several typographical errors in both the PDF and th...Correction to:npj Computational Materials https://doi.org/10.1038/s41524-023-00966-0,published online 16 February 2023 The original version of this Article contained several typographical errors in both the PDF and the HTML versions.In the first paragraph of‘Introduction’,the sentence‘(The phrase was coined by Nobel laureate Francis Crick in his book What Mad Pursuit:A personal view of scientific discovery)’duplicated reference 1.The sentence and brackets have been removed in the corrected version.展开更多
基金the Swiss National Science Foundation (via grant no. 200021E_193642, grant no. 200021-232306, and the NCCR MUST)ETH Zurich (via collaborative grant 23-2ETH-050) for financial support+7 种基金MP, OV, and MB further acknowledge the Research Council of Finland for financial support (including projects 326291, 330118, and 341288)TF acknowledges funding by the Deutsche Forschungsgemeinschaft within CRC 1477 “Light-Matter Interactions at Interfaces” (project number 441234705)PHWS acknowledges support from the Swedish Research Council through project 2018-00740FRNCM acknowledges the Swedish Research Council (2018-00234 and 2019-06092) and the Carl Tryggers Stiftelse för Vetenskaplig Forskning (CTS 19-227)JAS acknowledges the Swedish Research Council (2023-06350)the Göran Gustafsson Foundation (2044)the Carl Tryggers Stiftelse för Vetenskaplig Forskning (CTS 21-1427)The Maloja instrument received funding from the Swiss National Science Foundation through R’Equip Grant No. 206021_182988. We thank the IT Services Group (ISG) of the Department of Physics at ETH Zurich for the excellent support and management of the computing hardware on which the spring software has been developed and tested.
文摘Coherent Diffraction Imaging(CDI)is an experimental technique to image isolated structures by recording the scattered light.The sample density can be recovered from the scattered field through a Fourier Transform operation.However,the phase of the field is lost during the measurement and has to be algorithmically retrieved.Here we present SPRING,an analysis framework tailored to X-ray Free Electron Laser(XFEL)single-shot single-particle diffraction data that implements the Memetic Phase Retrieval method to mitigate the shortcomings of conventional algorithms.We benchmark the approach on data acquired in two experimental campaigns at SwissFEL and European XFEL.Results reveal unprecedented stability and resilience of the algorithm’s behavior on the input parameters,and the capability of identifying the solution in conditions hardly treatable with conventional methods.A user-friendly implementation of SPRING is released as open-source software,aiming at being a reference tool for the CDI community at XFEL and synchrotron facilities.
文摘Single-shot coherent diffraction imaging of isolated nanosized particles has seen remarkable success in recent years,yielding in-situ measurements with ultra-high spatial and temporal resolution.The progress of high-repetition-rate sources for intense X-ray pulses has further enabled recording datasets containing millions of diffraction images,which are needed for the structure determination of specimens with greater structural variety and dynamic experiments.The size of the datasets,however,represents a monumental problem for their analysis.Here,we present an automatized approach for finding semantic similarities in coherent diffraction images without relying on human expert labeling.By introducing the concept of projection learning,we extend self-supervised contrastive learning to the context of coherent diffraction imaging and achieve a dimensionality reduction producing semantically meaningful embeddings that align with physical intuition.The method yields substantial improvements compared to previous approaches,paving the way toward real-time and large-scale analysis of coherent diffraction experiments at X-ray free-electron lasers.
文摘Correction to:npj Computational Materials https://doi.org/10.1038/s41524-023-00966-0,published online 16 February 2023 The original version of this Article contained several typographical errors in both the PDF and the HTML versions.In the first paragraph of‘Introduction’,the sentence‘(The phrase was coined by Nobel laureate Francis Crick in his book What Mad Pursuit:A personal view of scientific discovery)’duplicated reference 1.The sentence and brackets have been removed in the corrected version.
