  Autonomous synthesis and characterization of inorganic materials requires the
automatic and accurate analysis of X-ray diffraction spectra. For this task, we
designed a probabilistic deep learning algorithm to identify complex
multi-phase mixtures. At the core of this algorithm lies an ensemble
convolutional neural network trained on simulated diffraction spectra, which
are systematically augmented with physics-informed perturbations to account for
artifacts that can arise during experimental sample preparation and synthesis.
Larger perturbations associated with off-stoichiometry are also captured by
supplementing the training set with hypothetical solid solutions. Spectra
containing mixtures of materials are analyzed with a newly developed branching
algorithm that utilizes the probabilistic nature of the neural network to
explore suspected mixtures and identify the set of phases that maximize
confidence in the prediction. Our model is benchmarked on simulated and
experimentally measured diffraction spectra, showing exceptional performance
with accuracies exceeding those given by previously reported methods based on
profile matching and deep learning. We envision that the algorithm presented
here may be integrated in experimental workflows to facilitate the
high-throughput and autonomous discovery of inorganic materials.
