Advanced photon sources deliver X-ray with unprecedented brilliance and coherence, facilitating sophisticated multimodal characterization of diverse materials while simultaneously generating data volumes that far exceed the capacity of traditional, expert-driven analytical workflows. This growing gap between data acquisition and interpretation has become a critical bottleneck for timely scientific discovery, which creates a critical need for high-throughput and automated data analysis tools. Recent advances in artificial intelligence (AI) offer a transformative potential to address this challenge, enabling the direct mapping of complex experimental observations into scientific insights. However, existing AI-driven tools are remain largely fragmented and task-specific, limiting their adoption of modern multimodal experiments at the facility scale. Here we present IPSBrain, a unified AI-powered platform that serves as Intelligent Photon Source Brain for automated analysis of multimodal synchrotron experimental data. The platform integrated a series of novel, self-developed machine-learning-based models that enable automated data analysis from multiple experimental techniques, including diffraction, scattering, X-ray absorption, and tomograph. The platform is accessible through an intuitive web interface, which significantly lowers the technical barrier to advanced scientific data interpretation. By enabling end-to-end intelligent workflows from data acquisition to quantitative physical insight, IPSBrain supports high-throughput, reproducible, and scalable analysis aligned with the operational demands of modern synchrotron radiation sources. IPSBrain not only demonstrates a practical and extensible framework for AI-driven synchrotron data analysis but also establishes a transferable paradigm for intelligent data analysis of other large-scale research infrastructures, such as neutron sources and free-electron lasers.
Pang et al. (Fri,) studied this question.