A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra

• URL: http://arxiv.org/abs/2103.16664v1
• Date: Tue, 30 Mar 2021 20:13:01 GMT
• Title: A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra
• Authors: Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, Qingsong Tu, Gerbrand Ceder
• Abstract summary: We develop an ensemble convolutional neural network trained on simulated diffraction spectra to identify complex multi-phase mixtures. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods.
• Score: 4.240899165468488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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