Classification of diffraction patterns in single particle imaging experiments performed at X-ray free-electron lasers using a convolutional neural network

Single particle imaging (SPI) is a promising method for native structure determination which has undergone a fast progress with the development of X-ray Free-Electron Lasers. Large amounts of data are collected during SPI experiments, driving the need for automated data analysis. The necessary data analysis pipeline has a number of steps including binary object classification (single versus multiple hits). Classification and object detection are areas where deep neural networks currently outperform other approaches. In this work, we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting transfer learning, a moderate amount of data is sufficient for training of the neural network. We demonstrate here that a convolutional neural network (CNN) can be successfully used to classify data from SPI experiments. We compare the results of classification for the two different networks, with different depth and architecture, by applying them to the same SPI data with different data representation. The best results are obtained for YOLOv2 color images linear scale classification, which shows an accuracy of about 97% with the precision and recall of about 52% and 61%, respectively, which is in comparison to manual data classification.Comment: 23 pages, 6 figures, 3 table

Structure-transport correlation reveals anisotropic charge transport in coupled PbS nanocrystal superlattices

Semiconductive nanocrystals (NCs) can be self-assembled into ordered superlattices (SLs) to create artificial solids with emerging collective properties. Computational studies have predicted that properties such as electronic coupling or charge transport are determined not only by the individual NCs but also by the degree of their organization and structure. However, experimental proof for a correlation between structure and charge transport in NC SLs is still pending. Here, we perform X-ray nano-diffraction and apply Angular X-ray Cross-Correlation Analysis (AXCCA) to characterize the structures of coupled PbS NC SLs, which are directly correlated with the electronic properties of the same SL microdomains. We find strong evidence for the effect of SL crystallinity on charge transport and reveal anisotropic charge transport in highly ordered monocrystalline hexagonal close-packed PbS NC SLs, caused by the dominant effect of shortest interparticle distance. This implies that transport anisotropy should be a general feature of weakly coupled NC SLs.Comment: 49 pages, 20 Figure

An advanced workflow for single-particle imaging with the limited data at an X-ray free-electron laser

An improved analysis for single-particle imaging (SPI) experiments, using the limited data, is presented here. Results are based on a study of bacteriophage PR772 performed at the Atomic, Molecular and Optical Science instrument at the Linac Coherent Light Source as part of the SPI initiative. Existing methods were modified to cope with the shortcomings of the experimental data: inaccessibility of information from half of the detector and a small fraction of single hits. The general SPI analysis workflow was upgraded with the expectation-maximization based classification of diffraction patterns and mode decomposition on the final virus-structure determination step. The presented processing pipeline allowed us to determine the 3D structure of bacteriophage PR772 without symmetry constraints with a spatial resolution of 6.9 nm. The obtained resolution was limited by the scattering intensity during the experiment and the relatively small number of single hits

Analysis of single particle imaging experiments at X-ray Free-Electron Lasers

Single particle imaging (SPI) is a novel technique in X-ray science aimed at reconstructing the three-dimensional structure of nanoscale objects. Studying the inner structure of biological particles has become increasingly crucial, as evidenced by the pandemic of coronavirus disease (COVID-19) showing the necessity of scientific development in this field. The main advantage of this approach is that atomic structures can be resolved in their native environment without crystallization.SPI experiments require using electromagnetic radiation with a sub-nanometer wavelength (such as X-rays) sufficient to resolve the object’s internal structure. Because of the weak interaction of X-rays with matter, high coherence and photon flux are required to resolve the finest features in the object. Due to the extreme radiation dose, the biological particles are destroyed in the scattering process. To record a diffraction pattern corresponding to the undamaged structure, the X-ray pulse must have a duration shorter than the typical timescale of the destruction process. Therefore, high-brilliance synchrotron light sources could not be used due to insufficient coherent flux in a single pulse that is required for recording enough signal. The development of X-ray sources that have a high intensity and short pulse duration – X-ray free-electron lasers (XFELs) – overcome this challenge.In the SPI method, many identical particles of the investigated system are injected into the X-ray beam providing diffraction images in random orientations. The three-dimensional structure of the object is obtained by applying complex algorithms to the collected diffraction patterns. The size of one such dataset could exceed terabytes; this motivates the development and implementation of elaborate data analysis techniques that help to save expensive XFEL time and speed up data processing.The first two parts of this Thesis are based on the methodological development of the SPI data analysis workflow. The experimental data was collected from the virus PR772 at the Linac Coherent Light Source (LCLS) at SLAC, Stanford, USA in the frame of the SPI consortium. As a result of the developed methodology, which includes machine learning object classification, a three-dimensional virus structure with a resolution below 10 nanometers was reconstructed. The comparison of the result with the cryogenic microscopy studies showed similar features and an overall agreement between both techniques. Due to the complexity and cost of the SPI experiments, the preparation is a time- and effort-consuming process that requires high-level planning. The third part of this Thesis explores the optimization of set-up parameters through the simulation of the SPI experiment with tick-borne encephalitis virus. These simulations contributed to the success of an actual experiment performed at the European XFEL in Hamburg, Germany

The structure of tick-borne encephalitis virus determined at X-ray free-electron lasers. Simulations

The study of virus structures by X-ray free-electron lasers (XFELs) has attracted increased attention in recent decades. Such experiments are based on the collection of 2D diffraction patterns measured at the detector following the application of femtosecond X-ray pulses to biological samples. To prepare an experiment at the European XFEL, the diffraction data for the tick-borne encephalitis virus (TBEV) was simulated with different parameters and the optimal values were identified. Following the necessary steps of a well established data-processing pipeline, the structure of TBEV was obtained. In the structure determination presented, a priori knowledge of the simulated virus orientations was used. The efficiency of the proposed pipeline was demonstrated

Classification of diffraction patterns in single particle imaging experiments performed at x-ray free-electron lasers using a convolutional neural network

Single particle imaging (SPI) is a promising method of native structure determination, which has undergone fast progress with the development of x-ray free-electron lasers. Large amounts of data are collected during SPI experiments, driving the need for automated data analysis. The necessary data analysis pipeline has a number of steps including binary object classification (single versus non-single hits). Classification and object detection are areas where deep neural networks currently outperform other approaches. In this work, we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting transfer learning, a moderate amount of data is sufficient to train the neural network. We demonstrate here that a convolutional neural network can be successfully used to classify data from SPI experiments. We compare the results of classification for the two different networks, with different depth and architecture, by applying them to the same SPI data with different data representation. The best results are obtained for diffracted intensity represented by color images on a linear scale using YOLOv2 for classification. It shows an accuracy of about 95% with precision and recall of about 50% and 60%, respectively, in comparison to manual data classification

In situ characterization of crystallization and melting of soft, thermoresponsive microgels by small-angle X-ray scattering

Depending on the volume fraction and interparticle interactions, colloidal suspensions can form different phases, ranging from fluids, crystals, and glasses to gels. For soft microgels that are made from thermoresponsive polymers, the volume fraction can be tuned by temperature, making them excellent systems to experimentally study phase transitions in dense colloidal suspensions. However, investigations of phase transitions at high particle concentration and across the volume phase transition temperature in particular, are challenging due to the deformability and possibility for interpenetration between microgels. Here, we investigate the dense phases of composite core-shell microgels that have a small gold core and a thermoresponsive microgel shell. Employing Ultra Small-Angle X-ray Scattering, we make use of the strong scattering signal from the gold cores with respect to the almost negligible signal from the shells. By changing the temperature we study the freezing and melting transitions of the system in situ. Using Bragg peak analysis and the Williamson-Hall method, we characterize the phase transitions in detail. We show that the system crystallizes into an rhcp structure with different degrees of in-plane and out-of-plane stacking disorder that increase upon particle swelling. We further find that the melting process is distinctly different, where the system separates into two different crystal phases with different melting temperatures and interparticle interactions

Mitigating the photodegradation of all-inorganic mixed-halide perovskite nanocrystals by ligand exchange

We show that the decomposition of caesium lead halide perovskite nanocrystals under continuous X-ray illumination depends on the surface ligand. For oleic acid/oleylamine, we observe a fast decay accompanied by the formation of elemental lead and halogen. Upon surface functionalization with a metal porphyrin derivative, the decay is markedly slower and involves the disproportionation of lead to Pb$^0$ and Pb$^{3+}$. In both cases, the decomposition is preceded by a contraction of the atomic lattice, which appears to initiate the decay. We find that the metal porphyrin derivative induces a strong surface dipole on the nanocrystals, which we hold responsible for the altered and slower decomposition pathway. These results are important for application of lead halide perovskite nanocrystals in X-ray scintillators

In situ characterization of crystallization and melting of soft, thermoresponsive microgels by small-angle X-ray scattering

Depending on the volume fraction and interparticle interactions, colloidal suspensions can form different phases, ranging from fluids, crystals, and glasses to gels. For soft microgels that are made from thermoresponsive polymers, the volume fraction can be tuned by temperature, making them excellent systems to experimentally study phase transitions in dense colloidal suspensions. However, investigations of phase transitions at high particle concentration and across the volume phase transition temperature in particular, are challenging due to the deformability and possibility for interpenetration between microgels. Here, we investigate the dense phases of composite core-shell microgels that have a small gold core and a thermoresponsive microgel shell. Employing Ultra Small-Angle X-ray Scattering, we make use of the strong scattering signal from the gold cores with respect to the almost negligible signal from the shells. By changing the temperature we study the freezing and melting transitions of the system in situ. Using Bragg peak analysis and the Williamson-Hall method, we characterize the phase transitions in detail. We show that the system crystallizes into an rhcp structure with different degrees of in-plane and out-of-plane stacking disorder that increase upon particle swelling. We further find that the melting process is distinctly different, where the system separates into two different crystal phases with different melting temperatures and interparticle interactions