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XRD patterns are encoded into a low dimensional representation (mean, variance). While this offers a correlation with structural classification, we show that visualizing the latent space with respect to the reconstruction error of the VAE allows for novelty detection during an experiment.ДЄ Schematic VAE architecture. Next, we develop a dynamic visualization tool for experimental XRD patterns and the VAE latent space. Firstly, we identify regions of learned similarity and potentially degenerate solutions in our prior. Notably, this same synthetic dataset can be used to train a state-of-the-art classification model in tandem 18, 24. 1a) trained on a synthetic dataset as a prior 18, 23 to solve commonly occurring visualization and novelty detection challenges in XRD analysis. Here, we use a variational autoencoder (VAE) (Fig.

#X ray diffraction pattern manual#

In this way, scientists can invest essential resources into the most promising areas and focus their manual effort where human experience is valued most. aware of вЂwhat it doesnвЂ™t knowвЂ) could inform scientists about experiments that mandate further investigation due to failure modes or material novelty. An AI agent that is sensitive to novelty (i.e. Hence, the recognition of novelty in large amounts of high-throughput data is a key challenge for scientists, and subsequently the tools that they use. In the context of materials discovery, material novelty comprises unreported materials of certain chemical composition and crystal structure.

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structures that were absent in the training data or XRD patterns of phase mixtures which were not considered in the training data. Here, we define novelty as XRD patterns that are unknown to the AI, e.g. These priors stem from expert knowledge and expectation, and as such, even accurate, feed-forward classifiers can come to false conclusions when operating on new or novel data. simulated X-ray diffractograms of expected phases from crystallographic database entries that encompass the breadth of possible experimental non-idealities) that makes these AI agents so successful 18. Nonetheless, it is the domain knowledge and prior information (e.g. In contrast, experiment specific models have a more relevant, yet more narrow, distribution or training data, and thus struggle when encountering data outside of this distribution. Broader models operate without specific domain knowledge, such as predicted phases of an investigated materials or experimental measurement parameters, and can be directly applied to a broad suite of classification challenges without fine tuning 17, 18.

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In broader approaches, experimentally relevant domain knowledge can be integrated modularly, e.g.

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Further refinement of classification results could be achieved by model interpretation 9. Classification models are particularly promising, having been developed for a broad scope (classifying crystal system, space group, point group) 10, 11, 12, 15, and specific challenges integrating experimental information 13, 17, 18, 22. Recent progress has been made in using AI for unsupervised XRD dataset decomposition 6, 7, crystal structure classification 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, and integrating the latter with autonomous experimentation 21. for the identification of compositionвЂ“structure-relationships, is a challenging cognitive task that requires the ability to recognize patterns under the awareness of several constraints 5. Manual analysis of combinatorial datasets, i.e. Innovations in high-throughput and autonomous experimentation 1, 2, 3, 4 are exceedingly increasing the acquisition rate of data, particularly in the case of XRD.

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