  The discovery of new structural and functional materials is driven by phase
identification, often using X-ray diffraction (XRD). Automation has accelerated
the rate of XRD measurements, greatly outpacing XRD analysis techniques that
remain manual, time-consuming, error-prone, and impossible to scale. With the
advent of autonomous robotic scientists or self-driving labs, contemporary
techniques prohibit the integration of XRD. Here, we describe a computer
program for the autonomous characterization of XRD data, driven by artificial
intelligence (AI), for the discovery of new materials. Starting from structural
databases, we train an ensemble model using a physically accurate synthetic
dataset, which output probabilistic classifications -- rather than absolutes --
to overcome the overconfidence in traditional neural networks. This AI agent
behaves as a companion to the researcher, improving accuracy and offering
significant time savings. It was demonstrated on a diverse set of organic and
inorganic materials characterization challenges. This innovation is directly
applicable to inverse design approaches, robotic discovery systems, and can be
immediately considered for other forms of characterization such as spectroscopy
and the pair distribution function.
