Artificial neural network methods for the prediction of framework crystal structures of zeolites from XRD data

Tatlıer, Melkon

doi:10.1007/s00521-010-0386-4

Cited by 23 publications

(17 citation statements)

References 16 publications

(18 reference statements)

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“…While software for indexing and determining the space group exists, it requires substantial expertise and human input to obtain the correct results. Previous machine learning attempts in this field mostly considered the task of feature engineering (e.g., PCA [256][257][258][259] or manual featurization 260,261 ) or considered smaller datasets and shallower neural networks. In contrast, in ref.…”

Section: Structure Predictionmentioning

confidence: 99%

Recent advances and applications of machine learning in solid-state materials science

et al. 2019

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One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure-property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

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Section: Structure Predictionmentioning

confidence: 99%

Recent advances and applications of machine learning in solid-state materials science

et al. 2019

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“…Prior to the boom in the art of deep learning, a number of powder XRD-related modelling studies used a conventional ANN. However, most of the previously reported cases were dealing with various feature engineering skills, such as manual featurization (Tatlier, 2011;Kustrin et al, 2000), principal component analysis (PCA) (Obeidat et al, 2011;Mitsui & Satoh, 1997;Chen et al, 2005;Matos et al, 2007), partial leastsquares regression (PLSR) (Lee et al, 2007) and various special statistical approaches (Gilmore et al, 2004;Barr et al, 2004). Feature engineering can simply be thought of as data contraction, which is more precisely defined as data-dimension contraction.…”

Section: Introductionmentioning

confidence: 99%

Classification of crystal structure using a convolutional neural network

Park

Chung

Jung

et al. 2017

IUCrJ

180

194

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A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

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“…One example is the application of the deep CNN methods to X‐ray tomography which can improve the quality of the projections collected, thus increasing by at least tenfold, the final low‐dose fast acquisition X‐ray tomographic signal . Further examples include the application of machine learning approaches to image classification and pattern recognition in image analysis of protein crystals, X‐ray diffraction, X‐ray scattering, X‐ray spectroscopy, parameter‐space exploration, as well as X‐ray microtomography . At ALS this approach has been incorporated in a whole framework to address Images across Domains, Experiments, Algorithms and Learning (IDEAL), which is aimed at addressing a full set of pattern recognition and analysis problems …”

Section: Computational Big Data Approachesmentioning

confidence: 99%

Synchrotron Big Data Science

Wang

Steiner

Sepe

2018

Small

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The rapid development of synchrotrons has massively increased the speed at which experiments can be performed, while new techniques have increased the amount of raw data collected during each experiment. While this has created enormous new opportunities, it has also created tremendous challenges for national facilities and users. With the huge increase in data volume, the manual analysis of data is no longer possible. As a result, only a fraction of the data collected during the time- and money-expensive synchrotron beam-time is analyzed and used to deliver new science. Additionally, the lack of an appropriate data analysis environment limits the realization of experiments that generate a large amount of data in a very short period of time. The current lack of automated data analysis pipelines prevents the fine-tuning of beam-time experiments, further reducing their potential usage. These effects, collectively known as the "data deluge," affect synchrotrons in several different ways including fast data collection, available local storage, data management systems, and curation of the data. This review highlights the Big Data strategies adopted nowadays at synchrotrons, documenting this novel and promising hybridization between science and technology, which promise a dramatic increase in the number of scientific discoveries.

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Artificial neural network methods for the prediction of framework crystal structures of zeolites from XRD data

Cited by 23 publications

References 16 publications

Recent advances and applications of machine learning in solid-state materials science

Recent advances and applications of machine learning in solid-state materials science

Classification of crystal structure using a convolutional neural network

Synchrotron Big Data Science

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