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Recent Advances and Applications of Machine Learning in Materials Science and Engineering

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 13441

Special Issue Editor

Dr. Dorota Wilk-Kołodziejczyk

E-Mail Website
Guest Editor
Department of Applied Computer Science and Modelling, Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
Interests: prediction of mechanical properties; data analysis in material science; artificial intelligence in material science and metallurgy; automated microstructure classification; intelligent information and decision systems for metallurgy

Special Issue Information

Dear Colleagues,

Research is increasing on the subject of using machine-learning methods to analyze microstructure photos and data of metal alloy data and the values of mechanical parameters of castings in order to verify their correct implementation. This is an important research trend. In this regard, research is carried out at various levels of general and micro-/nanoscales. The selection of tools based on artificial intelligence is also a real challenge.

The fast growth of the topic and increasing interest in the field from researchers with expertise in the areas of material science and beyond are the main reasons for this Special Issue on “Recent Advances and Applications of Machine Learning in Materials Science and Engineering”. Contributions should focus on new achievements, both theoretical and experimental, relative to the neural networks, predictive algorithms, and data analysis in material science. The issue aims to provide a platform for researchers working in the field to disseminate their ideas on the design and characterization of new configurations, highlighting novel dynamic phenomena and exploring promising applications. It should also stimulate a cross-fertilization between researchers of this field with other readers of the journal, providing the opportunity to find potential new research directions.

Dr. Dorota Wilk-Kołodziejczyk
Guest Editor

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

  • prediction of mechanical properties
  • data analysis in material science
  • artificial intelligence in material science and metallurgy
  • automated microstructure classification
  • metal castings
  • alloy microstructure
  • phase constituent
  • morphology
  • image analysis
  • deep learning
  • nanoscale advances

Published Papers (7 papers)

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Research

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22 pages, 4615 KiB  
Article
Complex-Phase Steel Microstructure Segmentation Using UNet: Analysis across Different Magnifications and Steel Types
by Bishal Ranjan Swain, Dahee Cho, Joongcheul Park, Jae-Seung Roh and Jaepil Ko
Materials 2023, 16(23), 7254; https://doi.org/10.3390/ma16237254 - 21 Nov 2023
Cited by 1 | Viewed by 1138
Abstract
The quantification of the phase fraction is critical in materials science, bridging the gap between material composition, processing techniques, microstructure, and resultant properties. Traditional methods involving manual annotation are precise but labor-intensive and prone to human inaccuracies. We propose an automated segmentation technique [...] Read more.
The quantification of the phase fraction is critical in materials science, bridging the gap between material composition, processing techniques, microstructure, and resultant properties. Traditional methods involving manual annotation are precise but labor-intensive and prone to human inaccuracies. We propose an automated segmentation technique for high-tensile strength alloy steel, where the complexity of microstructures presents considerable challenges. Our method leverages the UNet architecture, originally developed for biomedical image segmentation, and optimizes its performance via careful hyper-parameter selection and data augmentation. We employ Electron Backscatter Diffraction (EBSD) imagery for complex-phase segmentation and utilize a combined loss function to capture both textural and structural characteristics of the microstructures. Additionally, this work is the first to examine the scalability of the model across varying magnifications and types of steel and achieves high accuracy in terms of dice scores demonstrating the adaptability and robustness of the model. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Materials Science and Engineering)
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22 pages, 7015 KiB  
Article
FEM-GAN: A Physics-Supervised Deep Learning Generative Model for Elastic Porous Materials
by Albert Argilaga
Materials 2023, 16(13), 4740; https://doi.org/10.3390/ma16134740 - 30 Jun 2023
Cited by 1 | Viewed by 1416
Abstract
X-ray μCT imaging is a common technique that is used to gain access to the full-field characterization of materials. Nevertheless, the process can be expensive and time-consuming, thus limiting image availability. A number of existing generative models can assist in mitigating this [...] Read more.
X-ray μCT imaging is a common technique that is used to gain access to the full-field characterization of materials. Nevertheless, the process can be expensive and time-consuming, thus limiting image availability. A number of existing generative models can assist in mitigating this limitation, but they often lack a sound physical basis. This work presents a physics-supervised generative adversarial networks (GANs) model and applies it to the generation of X-ray μCT images. FEM simulations provide physical information in the form of elastic coefficients. Negative X-ray μCT images of a Hostun sand were used as the target material. During training, image batches were evaluated with nonparametric statistics to provide posterior metrics. A variety of loss functions and FEM evaluation frequencies were tested in a parametric study. The results show, that in several test scenarios, FEM-GANs-generated images proved to be better than the reference images for most of the elasticity coefficients. Although the model failed at perfectly reproducing the three out-of-axis coefficients in most cases, the model showed a net improvement with respect to the GANs reference. The generated images can be used in data augmentation, the calibration of image analysis tools, filling incomplete X-ray μCT images, and generating microscale variability in multiscale applications. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Materials Science and Engineering)
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17 pages, 8406 KiB  
Article
Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks
by Dongshuang Li, Shaohua You, Qinzhuo Liao, Gang Lei, Xu Liu, Weiqing Chen, Huijian Li, Bo Liu and Xiaoxi Guo
Materials 2023, 16(13), 4668; https://doi.org/10.3390/ma16134668 - 28 Jun 2023
Viewed by 950
Abstract
The permeability of porous materials determines the fluid flow rate and aids in the prediction of their mechanical properties. This study developed a novel approach that combines the discrete cosine transform (DCT) and artificial neural networks (ANN) for permeability analysis and prediction in [...] Read more.
The permeability of porous materials determines the fluid flow rate and aids in the prediction of their mechanical properties. This study developed a novel approach that combines the discrete cosine transform (DCT) and artificial neural networks (ANN) for permeability analysis and prediction in digital rock images, focusing on nanoscale porous materials in shale formations. The DCT effectively captured the morphology and spatial distribution of material structure at the nanoscale and enhanced the computational efficiency, which was crucial for handling the complexity and high dimensionality of the digital rock images. The ANN model, trained using the Levenberg–Marquardt algorithm, preserved essential features and demonstrated exceptional accuracy for permeability prediction from the DCT-processed rock images. Our approach offers versatility and efficiency in handling diverse rock samples, from nanoscale shale to microscale sandstone. This work contributes to the comprehension and exploitation of unconventional resources, especially those preserved in nanoscale pore structures. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Materials Science and Engineering)
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14 pages, 3702 KiB  
Article
Optimization of Chaboche Material Parameters with a Genetic Algorithm
by Nejc Dvoršek, Iztok Stopeinig and Simon Klančnik
Materials 2023, 16(5), 1821; https://doi.org/10.3390/ma16051821 - 22 Feb 2023
Cited by 4 | Viewed by 1646
Abstract
The main objective of this study is to research and develop a genetic algorithm (GA) for optimizing Chaboche material model parameters within an industrial environment. The optimization is based on 12 experiments (tensile, low-cycle fatigue, and creep) that are performed on the material, [...] Read more.
The main objective of this study is to research and develop a genetic algorithm (GA) for optimizing Chaboche material model parameters within an industrial environment. The optimization is based on 12 experiments (tensile, low-cycle fatigue, and creep) that are performed on the material, and corresponding finite element models were created using Abaqus. Comparing experimental and simulation data is the objective function that the GA is minimizing. The GA’s fitness function makes use of a similarity measure algorithm to compare the results. Chromosome genes are represented with real-valued numbers within defined limits. The performance of the developed GA was evaluated using different population sizes, mutation probabilities, and crossover operators. The results show that the population size had the most significant impact on the performance of the GA. With a population size of 150, a mutation probability of 0.1, and two-point crossover, the GA was able to find a suitable global minimum. Comparing it to the classic trial and error approach, the GA improves the fitness score by 40%. It can deliver better results in a shorter time and offer a high degree of automation not present in the trial and error approach. Additionally, the algorithm is implemented in Python to minimize the overall cost and ensure its upgradability in the future. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Materials Science and Engineering)
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11 pages, 2260 KiB  
Article
Study on the Automatic Identification of ABX3 Perovskite Crystal Structure Based on the Bond-Valence Vector Sum
by Laisheng Zhang, Zhong Zhuang, Qianfeng Fang and Xianping Wang
Materials 2023, 16(1), 334; https://doi.org/10.3390/ma16010334 - 29 Dec 2022
Cited by 8 | Viewed by 2187
Abstract
Perovskite materials have a variety of crystal structures, and the properties of crystalline materials are greatly influenced by geometric information such as the space group, crystal system, and lattice constant. It used to be mostly obtained using calculations based on density functional theory [...] Read more.
Perovskite materials have a variety of crystal structures, and the properties of crystalline materials are greatly influenced by geometric information such as the space group, crystal system, and lattice constant. It used to be mostly obtained using calculations based on density functional theory (DFT) and experimental data from X-ray diffraction (XRD) curve fitting. These two techniques cannot be utilized to identify materials on a wide scale in businesses since they require expensive equipment and take a lot of time. Machine learning (ML), which is based on big data statistics and nonlinear modeling, has advanced significantly in recent years and is now capable of swiftly and reliably predicting the structures of materials with known chemical ratios based on a few key material-specific factors. A dataset encompassing 1647 perovskite compounds in seven crystal systems was obtained from the Materials Project database for this study, which used the ABX3 perovskite system as its research object. A descriptor called the bond-valence vector sum (BVVS) is presented to describe the intricate geometry of perovskites in addition to information on the usual chemical composition of the elements. Additionally, a model for the automatic identification of perovskite structures was built through a comparison of various ML techniques. It is possible to identify the space group and crystal system using just a small dataset of 10 feature descriptors. The highest accuracy is 0.955 and 0.974, and the highest correlation coefficient (R2) value of the lattice constant can reach 0.887, making this a quick and efficient method for determining the crystal structure. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Materials Science and Engineering)
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16 pages, 6351 KiB  
Article
Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods
by Adam Tchórz, Krzysztof Korona, Izabela Krzak, Adam Bitka, Marzanna Książek, Krzysztof Jaśkowiec, Marcin Małysza, Mirosław Głowacki and Dorota Wilk-Kołodziejczyk
Materials 2022, 15(22), 8254; https://doi.org/10.3390/ma15228254 - 21 Nov 2022
Viewed by 1433
Abstract
This paper presents an assessment of the possibility of using digital image classifiers for tomographic images concerning ductile iron castings. The results of this work can help the development of an efficient system suggestion allowing for decision making regarding the qualitative assessment of [...] Read more.
This paper presents an assessment of the possibility of using digital image classifiers for tomographic images concerning ductile iron castings. The results of this work can help the development of an efficient system suggestion allowing for decision making regarding the qualitative assessment of the casting process parameters. Special attention should be focused on the fact that automatic classification in the case of ductile iron castings is difficult to perform. The biggest problem in this aspect is the high similarity of the void image, which may be a sign of a defect, and the nodular graphite image. Depending on the parameters, the tests on different photos may look similar. Presented in this article are test scenarios of the module analyzing two-dimensional tomographic images focused on the comprehensive assessment by convolutional neural network models, which are designed to classify the provided image. For the purposes of the tests, three such models were created, different from each other in terms of architecture and the number of hyperparameters and trainable parameters. The described study is a part of the decision-making system, supporting the process of qualitative analysis of the obtained cast iron castings. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Materials Science and Engineering)
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Review

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30 pages, 16772 KiB  
Review
Application of Machine Learning in Material Synthesis and Property Prediction
by Guannan Huang, Yani Guo, Ye Chen and Zhengwei Nie
Materials 2023, 16(17), 5977; https://doi.org/10.3390/ma16175977 - 31 Aug 2023
Cited by 4 | Viewed by 3185
Abstract
Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can [...] Read more.
Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Materials Science and Engineering)
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