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

Wang, Hong; Xie, Yunchao; Li, Dawei; Deng, Heng; Zhao, Yunxin; Xin, Ming; Lin, Jian

doi:10.1021/acs.jcim.0c00020

Cited by 83 publications

(99 citation statements)

References 32 publications

(77 reference statements)

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“…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. 135 …”

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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“…As a final step, background and noise are added. Previous works extracted these components from measured signals (Wang et al, 2020) but we model the background using a highorder polynomial function (Chebyshev) and uncertainties of the measurement are represented by Gaussian noise, the same as in the work of Lee et al (2020). The inclusion of Gaussian noise in the simulated scans is necessary so that the network is able to distinguish between diffraction peaks and noise when applied to measured data.…”

Section: Addition Of Further Effectsmentioning

confidence: 99%

“…In application, highly specialized machine-learning algorithms, such as the non-negative matrix factorization (NMF) (Long et al, 2009) approach, as well as neural-network structures (Park et al, 2017) have already demonstrated good performance for the automatic analysis of XRD data. While the NMF approach learns to describe the diffraction pattern as a linear combination of phases with different fractions, neural networks interpret the diffraction scans as one-dimensional images and detect phases based on intensities under certain 2 angles (Oviedo et al, 2019;Wang et al, 2020). Previous works show that machine-and deep-learning models can be applied for space-group, extinction-group and crystal-system prediction (Oviedo et al, 2019;Park et al, 2017), and the assignment of diffraction patterns to their respective phases (Long et al, 2009;Wang et al, 2020;Stanev et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…While the NMF approach learns to describe the diffraction pattern as a linear combination of phases with different fractions, neural networks interpret the diffraction scans as one-dimensional images and detect phases based on intensities under certain 2 angles (Oviedo et al, 2019;Wang et al, 2020). Previous works show that machine-and deep-learning models can be applied for space-group, extinction-group and crystal-system prediction (Oviedo et al, 2019;Park et al, 2017), and the assignment of diffraction patterns to their respective phases (Long et al, 2009;Wang et al, 2020;Stanev et al, 2018). Recently, Lee et al (2020) developed the first neural-network model to identify up to three phases that occur proportionally in a mixture (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing deep-learning training for phase identification in powder X-ray diffractograms

Schuetzke

Benedix

Mikut

et al. 2021

Int Union Crystallogr J

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Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of d spacings and related intensities to the measured data. Most automated approaches apply some iterative procedure for the search/match process but fail to be generally reliable yet without the manual validation step of an expert. Recent advances in the field of machine and deep learning have led to the development of algorithms for use with diffraction patterns and are producing promising results in some applications. A limitation, however, is that thousands of training samples are required for the model to achieve a reliable performance and not enough measured samples are available. Accordingly, a framework for the efficient generation of thousands of synthetic XRD scans is presented which considers typical effects in realistic measurements and thus simulates realistic patterns for the training of machine- or deep-learning models. The generated data set can be applied to any machine- or deep-learning structure as training data so that the models learn to analyze measured XRD data based on synthetic diffraction patterns. Consequently, we train a convolutional neural network with the simulated diffraction patterns for application with iron ores or cements compounds and prove robustness against varying unit-cell parameters, preferred orientation and crystallite size in synthetic, as well as measured, XRD scans.

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

Cited by 83 publications

References 32 publications

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

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

Enhancing deep-learning training for phase identification in powder X-ray diffractograms

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

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