2022
DOI: 10.1002/aisy.202200042
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Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction

Abstract: Herein, data‐driven symmetry identification, property prediction, and low‐dimensional embedding from powder X‐Ray diffraction (XRD) patterns of inorganic crystal structure database (ICSD) and materials project (MP) entries are reported. For this purpose, a fully convolutional neural network (FCN), transformer encoder (T‐encoder), and variational autoencoder (VAE) are used. The results are compared to those obtained from a well‐established crystal graph convolutional neural network (CGCNN). A task‐specified sma… Show more

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Cited by 18 publications
(12 citation statements)
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References 63 publications
(150 reference statements)
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“…The poor performance of the models trained without labeled data can be attributed to the significant differences between the synthetic XRD patterns used in training and the μ-XRD data obtained from experiments. In previous studies, experimental data used for evaluating DNN models were typically collected from solid samples in ambient environments. In such cases, high-quality X-ray diffraction peaks from different planes were easily obtainable, which could be fitted to the synthetic data generated from the crystallographic structure. However, μ-XRD data obtained from hydrothermal fluid systems only exhibits a few primary diffraction peaks with low-index ( hkl ) lattice planes, even under the best circumstances, due to several adverse factors, including the small diffraction volume with microbeam, low exposure time, poorly crystalline samples, preferred orientation or large crystal compared with beam size, and overexposure in 2D images (extra bright spot, see Figure S6).…”
Section: Resultsmentioning
confidence: 99%
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“…The poor performance of the models trained without labeled data can be attributed to the significant differences between the synthetic XRD patterns used in training and the μ-XRD data obtained from experiments. In previous studies, experimental data used for evaluating DNN models were typically collected from solid samples in ambient environments. In such cases, high-quality X-ray diffraction peaks from different planes were easily obtainable, which could be fitted to the synthetic data generated from the crystallographic structure. However, μ-XRD data obtained from hydrothermal fluid systems only exhibits a few primary diffraction peaks with low-index ( hkl ) lattice planes, even under the best circumstances, due to several adverse factors, including the small diffraction volume with microbeam, low exposure time, poorly crystalline samples, preferred orientation or large crystal compared with beam size, and overexposure in 2D images (extra bright spot, see Figure S6).…”
Section: Resultsmentioning
confidence: 99%
“…The theoretical XRD patterns were generated by mixing two and three end member patterns (bastnaesite, calcite, and rhenium (Re) metal). This mixing was conducted multiple times for every combination of phases with different ratios. Small number of labeled experimental XRD patterns were also produced for generating data sets (see additional information in the Supporting Information for detailed composition of training data sets).…”
Section: Methodology and Experimentsmentioning
confidence: 99%
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“…In this paper, we aim to explore whether we can develop a deep learning algorithm to predict the XRD spectrum from the composition alone, which can then be used for fast large-scale structure-oriented screening in modern computational generative materials design. Since our predicted XRDs can be fed to downstream algorithms to predict their structural dimensionality, crystal systems, and space groups, our XRD prediction algorithm can be very useful for screening potential new materials with only their composition information, which can significantly narrow down the crystal structure prediction and Density Functional Theory (DFT) calculations effort. Our XRD prediction algorithm can also be potentially used for the unsupervised discovery of new materials with similar properties to known materials .…”
Section: Introductionmentioning
confidence: 99%