Deep learning for ultrafast X-ray scattering and imaging with intense X-ray FEL pulses

The advent of X-ray Free Electron Lasers (XFELs) has opened unprecedented opportunities for advances in the physical, chemical, and biological sciences. With their state-of-the-art methodologies and ultrashort, and intense X-ray pulses, XFELs propel X-ray science into a new era, surpassing the capabilities of traditional light sources. Ultrafast X-ray scattering and imaging techniques leverage the coherence of these intense pulses to capture nanoscale structural dynamics with femtosecond spatial-temporal resolution. However, spatial and temporal resolutions remain limited by factors such as intrinsic fluctuations and jitters in the Self-Amplified Spontaneous Emission (SASE) mode, relatively low coherent scattering cross-sections, the need for high-performance, single-photon-sensitive detectors, effective sample delivery techniques, low parasitic X-ray instrumentation, and reliable data analysis methods. Furthermore, the high-throughput data flow from high-repetition rate XFEL facilities presents significant challenges. Therefore, more investigation is required to determine how Artificial Intelligence (AI) can support data science in this situation. In recent years, deep learning has made significant strides across various scientific disciplines. To illustrate its direct influence on ultrafast X-ray science, this article provides a comprehensive overview of deep learning applications in ultrafast X-ray scattering and imaging, covering both theoretical foundations and practical applications. It also discusses the current status, limitations, and future prospects, with an emphasis on its potential to drive advancements in fourth-generation synchrotron radiation, ultrafast electron diffraction, and attosecond X-ray studies.