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Artificial Intelligence for Advanced Materials Research
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Artificial Intelligence for Advanced Materials Research

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Materials Characterization".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 6940

Special Issue Editors

Dr. Alisa Stratulat

E-Mail
Guest Editor
MIPAR Image Analysis, Columbus, OH, USA
Interests: materials testing and characterization; image analysis and classification; deep learning; artificial intelligence; computer vision; smart materials; materials in industry; innovation in materials
Dr. Stefan G. Stanciu

E-Mail Website
Guest Editor
Politehnica University of Bucharest, Bucuresti, Romania
Interests: microscopy; nanoscopy; image processing; image analysis; artificial intelligence; biophotonics; advanced materials

Special Issue Information

Dear Colleagues,

In the past decade, Artificial Intelligence (AI) has taken the world by storm. Deep Learning methods based on convolutional neural networks currently lead machine learning benchmarks in computer vision, speech recognition, machine game play, and various other fields. One of their key advantages consists in the power to process vast amounts of data to result in conclusions matching those of human experts. Such capabilities yield tremendous impact in a wide variety of fields. For example, the current trends suggest that medicine will be completely transformed over the next few years, given that AI tools embedded in smartphones, smartwatches or other wearables can contribute to the timely detection of silent but deadly pathologies and precursory signs, and will thus allow implementing timelier and more efficient therapeutic strategies compared to current ones. When it comes to materials science, AI makes it possible to replace traditional trial-and-error approaches with novel ones that provide the optimal solution for a given problem at the click of a mouse-button. AI tools help researchers synthetise new materials faster by intelligently optimizing the composition and extracting meaningful data from any type of measurement record: micrographs, time-lapse microscopy movies, spectra, plots, etc. From identifying and classifying defects to distinguishing complicated features or optimizing microstructures, AI allows researchers to perform an automated data analysis, without operator bias or any data analysis expertise. Furthermore, recent results suggest that not long from now, AI will augment the usefulness of advanced systems for characterization to the point where it will be possible to measure properties that lie outside the resolving power of any available hardware instrument (e.g., microscopes, spectrometers, etc.). Furthermore, AI methods for cross-modality imaging will enable large scale (virtual) access to latest-hour characterization tools, enabling novel research routes and perspectives at a scale that is still difficult to comprehend. This Special Issue welcomes original research articles presenting significant advances in the field of AI methods, applications, and case studies for the synthesis and characterization of advanced materials, as well as timely reviews and perspectives addressing key problems in the field.

Dr. Alisa Stratulat
Dr. Stefan G. Stanciu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • advanced materials
  • material synthesis
  • material characterization
  • material testing
  • image analysis
  • nanomaterials
  • nanomedicine
  • sensing
  • environment

Published Papers (3 papers)

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Research

15 pages, 29668 KiB  
Article
Processing Laue Microdiffraction Raster Scanning Patterns with Machine Learning Algorithms: A Case Study with a Fatigued Polycrystalline Sample
by Peng Rong, Fengguo Zhang, Qing Yang, Han Chen, Qiwei Shi, Shengyi Zhong, Zhe Chen and Haowei Wang
Materials 2022, 15(4), 1502; https://doi.org/10.3390/ma15041502 - 17 Feb 2022
Cited by 1 | Viewed by 1303
Abstract
The massive amount of diffraction images collected in a raster scan of Laue microdiffraction calls for a fast treatment with little if any human intervention. The conventional method that has to index diffraction patterns one-by-one is laborious and can hardly give real-time feedback. [...] Read more.
The massive amount of diffraction images collected in a raster scan of Laue microdiffraction calls for a fast treatment with little if any human intervention. The conventional method that has to index diffraction patterns one-by-one is laborious and can hardly give real-time feedback. In this work, a data mining protocol based on unsupervised machine learning algorithm was proposed to have a fast segmentation of the scanning grid from the diffraction patterns without indexation. The sole parameter that had to be set was the so-called “distance threshold” that determined the number of segments. A statistics-oriented criterion was proposed to set the “distance threshold”. The protocol was applied to the scanning images of a fatigued polycrystalline sample and identified several regions that deserved further study with, for instance, differential aperture X-ray microscopy. The proposed data mining protocol is promising to help economize the limited beamtime. Full article
(This article belongs to the Special Issue Artificial Intelligence for Advanced Materials Research)
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17 pages, 12640 KiB  
Article
Image Processing of Mg-Al-Sn Alloy Microstructures for Determining Phase Ratios and Grain Size and Correction with Manual Measurement
by Ali Ercetin, Fatih Akkoyun, Ercan Şimşir, Danil Yurievich Pimenov, Khaled Giasin, Manjunath Patel Gowdru Chandrashekarappa, Avinash Lakshmikanthan and Szymon Wojciechowski
Materials 2021, 14(17), 5095; https://doi.org/10.3390/ma14175095 - 06 Sep 2021
Cited by 18 | Viewed by 2825
Abstract
The study of microstructures for the accurate control of material properties is of industrial relevance. Identification and characterization of microstructural properties by manual measurement are often slow, labour intensive, and have a lack of repeatability. In the present work, the intermetallic phase ratio [...] Read more.
The study of microstructures for the accurate control of material properties is of industrial relevance. Identification and characterization of microstructural properties by manual measurement are often slow, labour intensive, and have a lack of repeatability. In the present work, the intermetallic phase ratio and grain size in the microstructure of known Mg-Sn-Al alloys were measured by computer vision (CV) technology. New Mg (Magnesium) alloys with different alloying element contents were selected as the work materials. Mg alloys (Mg-Al-Sn) were produced using the hot-pressing powder metallurgy technique. The alloys were sintered at 620 °C under 50 MPa pressure in an argon gas atmosphere. Scanning electron microscopy (SEM) images were taken for all the fabricated alloys (three alloys: Mg-7Al-5Sn, Mg-8Al-5Sn, Mg-9Al-5Sn). From the SEM images, the grain size was counted manually and automatically with the application of CV technology. The obtained results were evaluated by correcting automated grain counting procedures with manual measurements. The accuracy of the automated counting technique for determining the grain count exceeded 92% compared to the manual counting procedure. In addition, ASTM (American Society for Testing and Materials) grain sizes were accurately calculated (approximately 99% accuracy) according to the determined grain counts in the SEM images. Hence, a successful approach was proposed by calculating the ASTM grain sizes of each alloy with respect to manual and automated counting methods. The intermetallic phases (Mg17Al12 and Mg2Sn) were also detected by theoretical calculations and automated measurements. The accuracy of automated measurements for Mg17Al12 and Mg2Sn intermetallic phases were over 95% and 97%, respectively. The proposed automatic image processing technique can be used as a tool to track and analyse the grain and intermetallic phases of the microstructure of other alloys such as AZ31 and AZ91 magnesium alloys, aluminium, titanium, and Co alloys. Full article
(This article belongs to the Special Issue Artificial Intelligence for Advanced Materials Research)
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18 pages, 4955 KiB  
Article
Optimizing Optical Film Lamination to Enhance the Luminance of TFT-LCD Displays Using the Taguchi-NNGA Method
by Yungho Leu and Chia-Ming Lin
Materials 2021, 14(16), 4481; https://doi.org/10.3390/ma14164481 - 10 Aug 2021
Viewed by 2040
Abstract
Luminance is an essential quality of a TFT-LCD display. Manufacturers have attempted to improve the soft-to-hard lamination stage to enhance the luminance of their TFT-LCD displays. In addition, many customers have complained about the insufficient luminance of the TFT-LCD displays of the case [...] Read more.
Luminance is an essential quality of a TFT-LCD display. Manufacturers have attempted to improve the soft-to-hard lamination stage to enhance the luminance of their TFT-LCD displays. In addition, many customers have complained about the insufficient luminance of the TFT-LCD displays of the case company. While product engineers have kept tuning the control factors in the soft-to-hard lamination stage through the trial and error method, the improvement of the luminance was not good enough. This study aims to assist the product engineers to fine-tune the settings of the control factors using a new method composed of the Taguchi method, a neural network, and a genetic algorithm. The confirmation experiments showed that the proposed method had increased the average luminance of the TFT-LCD displays from 17.03 to 25.15, which was higher than the required luminance value of 25. As a result, the number of complaints on the TFT-LCD displays had been significantly reduced. Full article
(This article belongs to the Special Issue Artificial Intelligence for Advanced Materials Research)
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