Materials Image Informatics Using Deep Learning

Agrawal, Ankit; Gopalakrishnan, Kasthurirangan; Choudhary, Alok

doi:10.1142/9789811204555_0006

Cited by 4 publications

(4 citation statements)

References 70 publications

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“…Many Artificial Intelligence (AI) applications and services are relying on DL to boost automation by executing analytical and physical activities without human interaction [18]. Authors in [1], said that DL is the rediscovery neural networks, which were algorithmically conceived in the '80s. During the recent years, the availability of big data and more capable computers has allowed these networks to become deeper.…”

Section: Experimental Workmentioning

confidence: 99%

“…In fact, practically everything in materials science is dependent on these PSPP interactions. There is a need to better comprehend this complicated system of PSPP connections to identify and build new improved materials with desired qualities [1,2]. DL has emerged as a useful method for big data analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The most prevalent method for EBSD indexing is the Hough transform-based method, which is based on computing angles between linear features derived from the direction pattern. The fundamental issue with this approach is that its performance degrades fast in the presence of noise [1].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Suker

2022

Eng. Technol. Appl. Sci. Res.

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Indexing of Electron Backscatter Diffraction (EBSD) is a well-established method of crystalline material characterization that provides phase and orientation information about the crystals on the material surface. A deep learning Convolutional Neural Network was trained to predict crystal orientation from the EBSD patterns based on the mean disorientation error between the predicted crystal orientation and the ground truth. The CNN is trained using EBSD images for different deformation conditions of AA5083.

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Section: Experimental Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Suker

2022

Eng. Technol. Appl. Sci. Res.

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“…Recently DL methods have been outperforming other machine learning techniques in numerous scientific fields, such as chemistry, physics, biology, and materials science [20,[28][29][30][31][32]. DL applications in MSE are still relatively new, and the field has not fully explored its potential, implications, and limitations.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances and Applications of Deep Learning Methods in Materials Science

Choudhary,

DeCost,

Chen

et al. 2021

Preprint

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Recent advances and applications of deep learning methods in materials science

et al. 2022

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Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

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Materials Image Informatics Using Deep Learning

Cited by 4 publications

References 70 publications

Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Recent Advances and Applications of Deep Learning Methods in Materials Science

Recent advances and applications of deep learning methods in materials science

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