A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

Lee, Jin Woong; Park, Woon Bae; Lee, Jin Hee; Singh, Satendra Pal; Sohn, Kee‐Sun

doi:10.1038/s41467-019-13749-3

Cited by 113 publications

(89 citation statements)

References 43 publications

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“…A convolutional neural network was trained by Sohn and colleagues [49] on simulated XRD results, to be used to distinguish phases of different material compositions. The model was able to recognize both phases and composition after training.…”

Section: Machine Learning (Ml) Techniques and Applicationsmentioning

confidence: 99%

Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage

2021

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The use of Mg-based compounds in solid-state hydrogen energy storage has a very high prospect due to its high potential, low-cost, and ease of availability. Today, solid-state hydrogen storage science is concerned with understanding the material behavior of different compositions and structure when interacting with hydrogen. Finding a suitable material has remained an elusive idea, and therefore, this review summarizes works by various groups, the milestones they have achieved, and the roadmap to be taken on the study of hydrogen storage using low-cost magnesium composites. Mg-based compounds are further examined from the perspective of artificial intelligence studies, which helps to improve prediction of their properties and hydrogen storage performance. There exist several techniques to improve the performance of Mg-based compounds: microstructure modification, use of catalytic additives, and composition regulation. Microstructure modification is usually achieved by employing different synthetic techniques like severe plastic deformation, high energy ball milling, and cold rolling, among others. These synthetic approaches are discussed herein. In this review, a discussion of key parameters and operating conditions are highlighted in a view to finding high storage capacity and faster kinetics. Furthermore, recent approaches like machine learning have found application in guiding the experimental design. Hence, this review paper also explores how machine learning techniques have been utilized to fasten the materials research. It is however noted that this study is not exhaustive in itself.

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Section: Machine Learning (Ml) Techniques and Applicationsmentioning

confidence: 99%

Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage

2021

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“…Toolboxes such as PyTorch [88] , Keras [89] provide user-friendly API and well-organized tutorial, which let the user without much knowledge can take advantage of the state-of-the-art deep learning techniques. [90][91][92][93][94]…”

Section: Toolboxmentioning

confidence: 99%

Deep Learning for Medical Materials: Review and Perspective

Qi¹,

Liu²

2021

ES Mater. Manuf.

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Deep learning and similar computational research approaches have cooperated with materials research for years. Especially in the last few years, with the fast evolution of machine learning and deep learning algorithms, a novel branch for material research is presented to be recognized, learned, practiced, adapted and perfected. Different conventional computational modeling methods, the deep learning approach assists material science and engineering from the aspect of data processing and analysis, rather than simulate the reality. However, mistakes always exist in all less explored concepts and developments. Here, in order to offer a better instruction of deep learning for materials scientists, several recent works on deep learning based material research were reviewed. The application of deep learning in material research is introduced and discussed. As an example of both general material and advanced material, medical material was discussed for the impact and future of deep learning methods. At last, we provided several insights on future work for both deep learning scientists, data scientists and materials scientists.

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“…where i = 1, …, n runs for all the crystal phases present in the sample, ( ) is the experimental profiles of the i-th pure crystal phases and ( ) is the background estimated from the experimental pattern. As a final note concerning recent developments in this field, the deep-learning technique based on Convolutional Neural Network (CNN) models has been applied for phase identification in multiphase inorganic compounds [72]. The network has been trained using synthetic XRPD patterns, and the approach has been validated on real experimental XRPD data, showing an accuracy of nearly 100% for phase identification and 86% for phase-fraction quantification.…”

Section: Qualitative and Quantitative Studiesmentioning

confidence: 99%

“…The drawback of this approach is the large computational effort necessary for preliminary operations. In fact, a training set of more than 1.5 × 10 6 synthetic XRPD profiles was necessary, even for a limited chemical space of 170 inorganic compounds belonging to the Sr-Li-Al-O quaternary compositional pool used in ref [72].…”

Section: Qualitative and Quantitative Studiesmentioning

confidence: 99%

Multivariate Analysis Applications in X-ray Diffraction

et al. 2020

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Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden in data, disclosing the possibility of automatic data processing even in absence of a priori structural knowledge. MA methods recently used in the field of X-ray diffraction are here reviewed and described, giving hints about theoretical background and possible applications. The use of MA in the framework of the modulated enhanced diffraction technique is described in detail.

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A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

Cited by 113 publications

References 43 publications

Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage

Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage

Deep Learning for Medical Materials: Review and Perspective

Multivariate Analysis Applications in X-ray Diffraction

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