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Crystals
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Artificial Intelligence for Crystal Growth and Characterization (2nd Edition)
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Artificial Intelligence for Crystal Growth and Characterization (2nd Edition)

A special issue of Crystals (ISSN 2073-4352). This special issue belongs to the section "Crystal Engineering".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 10096

Special Issue Editors

Dr. Saskia Schimmel

E-Mail Website
Guest Editor
Chair of Electron Devices (LEB), Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstraße 6, 91058 Erlangen, Germany
Interests: materials for efficient energy conversion and energy saving; crystallization of functional materials; bulk crystal growth, ammonothermal synthesis; solvothermal synthesis; (wide bandgap) semiconductors; metastable materials; numerical simulation of crystal growth processes; machine learning; artificial intelligence; in situ monitoring of crystal growth processes
Special Issues, Collections and Topics in MDPI journals
Dr. Natasha Dropka

E-Mail Website
Guest Editor
Head of Section Fundamental Description in Leibniz Institute for Crystal Growth (IKZ), Max Born str.2, 12489 Berlin, Germany
Interests: numerical simulation of crystal growth processes; machine learning; artificial intelligence; bulk crystal growth of semiconductors and oxides; crystal growth in magnetic fields
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The first volume of the Special Issue “Artificial Intelligence for Crystal Growth and Characterization” (https://www.mdpi.com/journal/crystals/special_issues/artificialintelligence_crystal) was an incredible success, and it is thus our pleasure to announce the second volume.

Machine learning and artificial intelligence methods in general have recently reached a stage at which they become increasingly useful to researchers in other fields. The goal of this Special Issue is to promote the use of those methods in the field of synthesis and characterization of crystalline materials. By bundling reports from all subdisciplines of crystal research and technology, this Special Issue aims to facilitate inspiration across all subdisciplines of crystal research. The sharing of developed software tools is particularly encouraged (for instance, by means of a public GitHub repository, or a private repository that can be shared upon request of interested authors, author’s webpages, etc.) but not mandatory.

Topics include but are not at all limited to: AI for high-throughput crystal characterization (e.g., AI-based evaluation of optical/SEM/AFM/X-ray images), AI for optimization of crystal growth processes, AI for identifying previously unrecognized but relevant process variables, AI for acceleration of numerical simulations related to crystal growth, AI for predictions of stability of crystalline materials, AI for prediction of synthesis outcomes, and AI for prediction of properties of crystalline materials. Topics beyond these suggestions are also very welcome, as long as they fit the outlined scope of the Special Issue.

Dr. Saskia Schimmel
Dr. Natasha Dropka
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. Crystals is an international peer-reviewed open access monthly 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
  • machine learning
  • crystal growth
  • characterization of crystalline materials
  • novel crystalline materials

Published Papers (6 papers)

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Research

14 pages, 2149 KiB  
Article
Computational Discovery of New Feasible Crystal Structures in Ce3O3N
by Jelena Zagorac, Johann Christian Schön, Branko Matović, Milan Pejić, Marija Prekajski Đorđević and Dejan Zagorac
Crystals 2023, 13(5), 774; https://doi.org/10.3390/cryst13050774 - 6 May 2023
Cited by 1 | Viewed by 1185
Abstract
Oxynitrides of cerium are expected to have many useful properties but have not been synthesized so far. We identified possible modifications of a not-yet-synthesized Ce3O3N compound, combining global search (GS) and data mining (DM) methods. Employing empirical potentials, structure [...] Read more.
Oxynitrides of cerium are expected to have many useful properties but have not been synthesized so far. We identified possible modifications of a not-yet-synthesized Ce3O3N compound, combining global search (GS) and data mining (DM) methods. Employing empirical potentials, structure candidates were obtained via global optimization on the energy landscape of Ce3O3N for different pressure values. Furthermore, additional feasible structure candidates were found using data mining of the ICSD database. The most promising structure candidates obtained were locally optimized at the ab initio level, and their E(V) curves were computed. The structure lowest in total energy, Ce3O3N-DM1, was found via local optimization starting from a data mining candidate and should be thermodynamically metastable up to high pressures. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization (2nd Edition))
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10 pages, 1821 KiB  
Article
Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal Copper
by Fanming Zeng and Yabin Yan
Crystals 2023, 13(3), 539; https://doi.org/10.3390/cryst13030539 - 21 Mar 2023
Cited by 2 | Viewed by 1214
Abstract
Microscale single-crystal copper is widely used in electronics, communications and other fields due to its excellent properties such as high ductility, high toughness and good conductivity. Therefore, it is particularly important to research its fatigue life. In order to explore the influence of [...] Read more.
Microscale single-crystal copper is widely used in electronics, communications and other fields due to its excellent properties such as high ductility, high toughness and good conductivity. Therefore, it is particularly important to research its fatigue life. In order to explore the influence of size effect, loading frequency and shear strain on the main slip surface on the fatigue life of microscale single-crystal copper based on in situ fatigue experimental data of microscale single-crystal copper, this paper used a BP neural network algorithm to construct a single-crystal copper fatigue life prediction network model. The data set included 14 groups of training data, with 11 groups as training sets and 3 groups as testing sets. The input characteristics were length, width, height, loading frequency and shear strain of the main sliding plane of a microscale single-crystal copper sample. The output characteristic was the fatigue life of microscale single-crystal copper. After training, the mean square error (MSE) of the model was 0.03, the absolute value error (MAE) was 0.125, and the correlation coefficient (R2) was 0.93271, indicating that the BP neural network algorithm can effectively predict the fatigue life of microscale single-crystal copper and has good generalization ability. This model can not only save the experimental time of fatigue life measurement of micro-scale single-crystal copper, but also optimize the properties of the material by taking equidistant points in the range of characteristic parameters. Therefore, the current study demonstrates an applicable and efficient methodology to evaluate the fatigue life of microscale materials in industrial applications. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization (2nd Edition))
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17 pages, 4744 KiB  
Article
Smart Design of Cz-Ge Crystal Growth Furnace and Process
by Natasha Dropka, Xia Tang, Gagan Kumar Chappa and Martin Holena
Crystals 2022, 12(12), 1764; https://doi.org/10.3390/cryst12121764 - 5 Dec 2022
Cited by 3 | Viewed by 2380
Abstract
The aim of this study was to evaluate the potential of the machine learning technique of decision trees to understand the relationships among furnace design, process parameters, crystal quality, and yield in the case of the Czochralski growth of germanium. The ultimate goal [...] Read more.
The aim of this study was to evaluate the potential of the machine learning technique of decision trees to understand the relationships among furnace design, process parameters, crystal quality, and yield in the case of the Czochralski growth of germanium. The ultimate goal was to provide the range of optimal values of 13 input parameters and the ranking of their importance in relation to their impact on three output parameters relevant to process economy and crystal quality. Training data were provided by CFD modelling. The variety of data was ensured by the Design of Experiments method. The results showed that the process parameters, particularly the pulling rate, had a substantially greater impact on the crystal quality and yield than the design parameters of the furnace hot zone. Of the latter, only the crucible size, the axial position of the side heater, and the material properties of the radiation shield were relevant. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization (2nd Edition))
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19 pages, 5650 KiB  
Article
Auto-Encoder Classification Model for Water Crystals with Fine-Tuning
by Hanan A. Hosni Mahmoud and Nada Ali Hakami
Crystals 2022, 12(11), 1667; https://doi.org/10.3390/cryst12111667 - 19 Nov 2022
Viewed by 1038
Abstract
Water is one of the important, though scarce, resources on earth. The 2021 World Water Resource Report claims that environmental challenges threaten the sustainability of water resources. Therefore, it is vital to screen water quality to sustain water resources. Water quality is related [...] Read more.
Water is one of the important, though scarce, resources on earth. The 2021 World Water Resource Report claims that environmental challenges threaten the sustainability of water resources. Therefore, it is vital to screen water quality to sustain water resources. Water quality is related to water crystal structure in its solid state. Intelligent models classify water crystals to predict their quality. Methods to analyze water crystals can aid in predicting water quality. Therefore, the major contribution of our research is the prediction of water crystal classes. The proposed model analyzes water crystals in solid states, employing image analysis and the deep learning method. The model specifies several feature groups, including crystal shape factors, solid-state features, crystal geometry and discrete cosine transform coefficients. The model utilizes feature fusion for better training. The proposed model utilized the EP water crystal dataset from the WC image depository and its accuracy was tested with the multi-feature Validation technique. The nature of our data inclined us to utilize F-Measure and sensitivity for the testing phase. Our proposed model outperformed other state of the art water crystal classification models by more than 6% in accuracy and 7% in f-measures, with performance exceeding 11% for triple feature fusion. Furthermore, our model was faster in training time (10% of the training time of the comparative models) and had 1.42 s classification time. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization (2nd Edition))
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14 pages, 2247 KiB  
Article
Deep Learning Classification of Crystal Structures Utilizing Wyckoff Positions
by Nada Ali Hakami and Hanan Ahmed Hosni Mahmoud
Crystals 2022, 12(10), 1460; https://doi.org/10.3390/cryst12101460 - 16 Oct 2022
Cited by 2 | Viewed by 2184
Abstract
In materials science, crystal lattice structures are the primary metrics used to measure the structure–property paradigm of a crystal structure. Crystal compounds are understood by the number of various atomic chemical settings, which are associated with Wyckoff sites. In crystallography, a Wyckoff site [...] Read more.
In materials science, crystal lattice structures are the primary metrics used to measure the structure–property paradigm of a crystal structure. Crystal compounds are understood by the number of various atomic chemical settings, which are associated with Wyckoff sites. In crystallography, a Wyckoff site is a point of conjugate symmetry. Therefore, features associated with the various atomic settings in a crystal can be fed into the input layers of deep learning models. Methods to analyze crystals using Wyckoff sites can help to predict crystal structures. Hence, the main contribution of our article is the classification of crystal classes using Wyckoff sites. The presented model classifies crystals using diffraction images and a deep learning method. The model extracts feature groups including crystal Wyckoff features and crystal geometry. In this article, we present a deep learning model to predict the stage of the crystal structure–property. The lattice parameters and the structure–property commotion values are used as inputs into the deep learning model for training. The structure–property value of a crystal with a lattice width value of one-half millimeter on average is used for learning. The model attains a considerable increase in speed and precision for the real structure–property prediction. The experimental results prove that our proposed model has a fast learning curve, and can have a key role in predicting the structure–property of compound structures. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization (2nd Edition))
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17 pages, 3651 KiB  
Article
Computerized Detection of Calcium Oxalate Crystal Progression
by Hanan A. Hosni Mahmoud
Crystals 2022, 12(10), 1450; https://doi.org/10.3390/cryst12101450 - 13 Oct 2022
Viewed by 1350
Abstract
Calcium oxalate crystals in plants can cause health issues such as kidney stones if ingested in large amounts. Calcium oxalate crystallizations affect approximately 4% of plants. Some of these crystallizations are more common, and human and animal ingestion can be avoided if the [...] Read more.
Calcium oxalate crystals in plants can cause health issues such as kidney stones if ingested in large amounts. Calcium oxalate crystallizations affect approximately 4% of plants. Some of these crystallizations are more common, and human and animal ingestion can be avoided if the degree of severity is detected at an early stage. Therefore, in this paper, we present a computerized method for detecting calcium oxalate crystallizations at an early stage, when chances for avoiding it are higher. In our research, electron micrograph processing techniques are used to extract features and measure the degree of crystallization progression in cases of crystalized plants and normal plants. A new fast search algorithm—ODS: One Direction Search—is proposed to detect calcium oxalate crystal progression. The calcium oxalate crystal progression is detected on the basis of electron micrographs of calcium oxalate crystals by means of a temporal test. We employed deep learning for feature extraction. The deep learning technique uses transfer learning, which allows the proposed detection model to be trained on only a small amount of data regarding calcium oxalate crystals for the determination of the presence of calcium oxalate crystals and the severity of the cases. The experimental results, using electron micrographs of 6900 clusters, demonstrated a success rate of 97.5% when detecting cases of calcium oxalate crystals. The simulation results of the new temporal algorithm show an enhancement of the speed by 70% compared to well-known temporal algorithms, and increased accuracy when computing PRSN against other algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization (2nd Edition))
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