Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

Oviedo, Felipe; Ren, Zekun; Sun, Shijing; Settens, Charles; Liu, Zhe; Hartono, Noor Titan Putri; Ramasamy, Savitha; DeCost, Brian; Tian, Siyu; Romano, Giuseppe; Kusne, A. Gilad; Buonassisi, Tonio

doi:10.1038/s41524-019-0196-x

Cited by 237 publications

(225 citation statements)

References 49 publications

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“…In this way, the authors generated new training data that correspond to the typically experimental distortions. 134 A similar approach was also chosen by Wang et al, who built a convolutional neural network (CNN) to identify MOFs based on their X-ray powder diffraction (XRPD) patterns. Wang et al predicted the patterns for MOFs in the Cambridge Structure Database (CSD) and then augmented their data set by creating new patterns by merging the main peaks of the predicted patterns with (shuffled) noise from pattern they measured in their own lab.…”

Section: Selecting the Data: Dealing With Little Imbalanced And Nonmentioning

confidence: 99%

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

et al. 2020

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By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.

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Section: Selecting the Data: Dealing With Little Imbalanced And Nonmentioning

confidence: 99%

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

et al. 2020

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“…[16] Oviedo and his colleagues proposed a machine learning approach to predict crystallographic dimensionality and space groups from a limited number of thin-film XRD spectra. [2] Angelo's research group developed a robust CNN model to classify crystal structures and also unfolded the internal behavior of the classification model through visualization. [14] Miller's research group implemented a CNN to determine crystallography trained on imaging and diffraction data.…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Wang

Xie

et al. 2020

J. Chem. Inf. Model.

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Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretic data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) spectra of metal-organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental spectra and shuffled, then merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra.For the first time, one-to-one material identification was achieved. The optimized model showed the highest identification accuracy of 96.7% for the Top 5 ranking among a dataset of 1012 MOFs.Neighborhood components analysis (NCA) on the experimental XRD spectra shows that the spectra from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD spectra. This CNN model trained by the data-augmentation technique would not only open numerous potential applications for identifying XRD spectra for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR.

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“…Liu et al 21 refined atomic pair distribution functions in a convolutional neural network (CNN) to classify SGs. For similar purposes, Park et al 22 , Vecsei et al 23 , Wang et al 24 , Oviedo et al 25 , and Aguiar et al 26 used powder X-ray diffraction (XRD) 1D curves, for which information such as peak positions, intensities, and fullwidths at half-maximum are mainly treated as the key input descriptors. In addition, Ziletti et al 27 (in a parent work of this study), Aguiar et al 28 , Kaufmann et al 29 , and Ziatdinov et al 30 developed DL models by extracting features from electron-beam based 2D DPs.…”

Section: Introductionmentioning

confidence: 99%

Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning

et al. 2020

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The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry. Despite their promise, most of these studies have been limited to identifying relatively few classes into which a target material may be grouped. On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e.g., up to 230 space groups), severely limiting its practical usage. Here, we demonstrate that a combined approach of shaping diffraction patterns and implementing them in a multistream DenseNet (MSDN) substantially improves the accuracy of classification. Even with an imbalanced dataset of 108,658 individual crystals sampled from 72 space groups, our model achieves 80.12 ± 0.09% space group classification accuracy, outperforming conventional benchmark models by 17–27 percentage points (%p). The enhancement can be largely attributed to the pattern shaping strategy, through which the subtle changes in patterns between symmetrically close crystal systems (e.g., monoclinic vs. orthorhombic or trigonal vs. hexagonal) are well differentiated. We additionally find that the MSDN architecture is advantageous for capturing patterns in a richer but less redundant manner relative to conventional convolutional neural networks. The proposed protocols in regard to both input descriptor processing and DL architecture enable accurate space group classification and thus improve the practical usage of the DL approach in crystal symmetry identification.

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Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

Cited by 237 publications

References 49 publications

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning

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