Delivering real-time multi-modal materials analysis with enterprise beamlines

Contemporary advancements in low-cost automation and computa-tion, reduced barrier to entry in developing artificial intelligence/ machine learning (AI/ML), and increased ability to represent com-plex materials in digital form have led to a number of accelerated materials discovery platforms. However, many of these approaches operate with completely rigid vertical integration in an isolated feedback loop using limited modalities. In order to make a substan-tial impact on discovering new energy materials, AI-driven experi-ments must operate collaboratively with each other and researchers and over multiple measurement modalities. Herein, we describe the potential for an "internet of things"approach to self-driving enter-prise beamlines that merges core information technologies, ro-botics, and multi-modal AI. The approach will enable full utility of light sources, collaborate effectively with other remote materials ac-celeration platforms, and help stride toward the world's energy future.