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The discovery of new structural and functional materials is driven by phaseidentification, often using X-ray diffraction (XRD). Automation has acceleratedthe rate of XRD measurements, greatly outpacing XRD analysis techniques thatremain manual, time-consuming, error-prone, and impossible to scale. With theadvent of autonomous robotic scientists or self-driving labs, contemporarytechniques prohibit the integration of XRD. Here, we describe a computerprogram for the autonomous characterization of XRD data, driven by artificialintelligence (AI), for the discovery of new materials. Starting from structuraldatabases, we train an ensemble model using a physically accurate syntheticdataset, which output probabilistic classifications -- rather than absolutes --to overcome the overconfidence in traditional neural networks. This AI agentbehaves as a companion to the researcher, improving accuracy and offeringsignificant time savings. It was demonstrated on a diverse set of organic andinorganic materials characterization challenges. This innovation is directlyapplicable to inverse design approaches, robotic discovery systems, and can beimmediately considered for other forms of characterization such as spectroscopyand the pair distribution function.

Crystallography companion agent for high-throughput materials discovery