Quality guided image recognition towards industrial materials diffusion

Wang, Hua; Liu, Cong

doi:10.1016/j.jvcir.2019.102608

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…For example, supervised machine learning is often used for classification or prediction. Image recognition is a typical example of supervised ML [ 27 , 28 , 29 ]. On the MNIST dataset, ML attempts to obtain a mapping relationship between pixels and labels.…”

Section: Introductionmentioning

confidence: 99%

A Review of Performance Prediction Based on Machine Learning in Materials Science

Liu

Huang

et al. 2022

Nanomaterials

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With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area.

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Section: Introductionmentioning

confidence: 99%

A Review of Performance Prediction Based on Machine Learning in Materials Science

Liu

Huang

et al. 2022

Nanomaterials

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Image-based machine learning for materials science

Zhang

Shao

2022

Journal of Applied Physics

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Materials research studies are dealing with a large number of images, which can now be facilitated via image-based machine learning techniques. In this article, we review recent progress of machine learning-driven image recognition and analysis for the materials and chemical domains. First, the image-based machine learning that facilitates the property prediction of chemicals or materials is discussed. Second, the analysis of nanoscale images including those from a scanning electron microscope and a transmission electron microscope is discussed, which is followed by the discussion about the identification of molecular structures via image recognition. Subsequently, the image-based machine learning works to identify and classify various practical materials such as metal, ceramics, and polymers are provided, and the image recognition for a range of real-scenario device applications such as solar cells is provided in detail. Finally, suggestions and future outlook for image-based machine learning for classification and prediction tasks in the materials and chemical science are presented. This article highlights the importance of the integration of the image-based machine learning method into materials and chemical science and calls for a large-scale deployment of image-based machine learning methods for prediction and classification of images in materials and chemical science.

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Quality guided image recognition towards industrial materials diffusion

Cited by 2 publications

References 28 publications

A Review of Performance Prediction Based on Machine Learning in Materials Science

A Review of Performance Prediction Based on Machine Learning in Materials Science

Image-based machine learning for materials science

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