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Crystals
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Artificial Intelligence for Crystal Growth and Characterization
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Artificial Intelligence for Crystal Growth and Characterization

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 23208

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. Wenhao Sun

E-Mail Website
Guest Editor
Materials Science and Engineering, University of Michigan, Ann Arbor, Ann Arbor, MI, USA
Interests: high-throughput density functional theory, applied thermodynamics, and materials informatics; metastable materials, nucleation and crystal growth, synthesis and processing science; materials discovery and design in uncharted chemical spaces
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,

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 at facilitating inspiration across all subdisciplines of crystal research. 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. Wenhao Sun
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 2100 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

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Published Papers (7 papers)

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Editorial

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3 pages, 167 KiB  
Editorial
Artificial Intelligence for Crystal Growth and Characterization
by Saskia Schimmel, Wenhao Sun and Natasha Dropka
Crystals 2022, 12(9), 1232; https://doi.org/10.3390/cryst12091232 - 1 Sep 2022
Cited by 5 | Viewed by 2324
Abstract
The Special Issue on “Artificial Intelligence for Crystal Growth and Characterization” comprises six original articles in this emerging field of research [...] Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)

Research

Jump to: Editorial

13 pages, 5183 KiB  
Article
Microstructure Representation Knowledge Graph to Explore the Twinning Formation
by Cheng Xie, Ziwen Pan and Chao Shu
Crystals 2022, 12(4), 466; https://doi.org/10.3390/cryst12040466 - 27 Mar 2022
Cited by 4 | Viewed by 2365
Abstract
Deformation twinning is an important mechanism of the plastic deformation of materials. The density of twins also affects the properties of the material. At present, the research methods of deformation twinning mainly depend on in situ EBSD, numerically investigated analysis and the finite [...] Read more.
Deformation twinning is an important mechanism of the plastic deformation of materials. The density of twins also affects the properties of the material. At present, the research methods of deformation twinning mainly depend on in situ EBSD, numerically investigated analysis and the finite element method. The application of machine learning methods to material microstructure research can shorten the time taken for material analysis. Machine learning methods are faced with the problem of the effective representation of the microstructure. We present a deformation twinning research method based on the representation of grain morphology features in a knowledge graph. We construct an autoencoder to extract grain morphology characteristics for building a grain knowledge graph. Then, a graph convolutional network (GCN) and fully connected network are developed to extract grain knowledge graph features and predict the twin density of materials subjected to specific tensile deformation. We use Mg-2Zn-3Li alloy as an experimental example to predict the twin density on three indexes of average grain size, twin boundaries density and average grain surface. The R2 score of the prediction result on the twin boundaries density is up to 0.510, and the R2 score of the average grain size and average grain surface is over 0.750. Therefore, the proposed method for deformation twinning research is effective and feasible. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)
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11 pages, 1722 KiB  
Article
Toward Precise n-Type Doping Control in MOVPE-Grown β-Ga2O3 Thin Films by Deep-Learning Approach
by Ta-Shun Chou, Saud Bin Anooz, Raimund Grüneberg, Klaus Irmscher, Natasha Dropka, Jana Rehm, Thi Thuy Vi Tran, Wolfram Miller, Palvan Seyidov, Martin Albrecht and Andreas Popp
Crystals 2022, 12(1), 8; https://doi.org/10.3390/cryst12010008 - 21 Dec 2021
Cited by 8 | Viewed by 3942
Abstract
In this work, we train a hybrid deep-learning model (fDNN, Forest Deep Neural Network) to predict the doping level measured from the Hall Effect measurement at room temperature and to investigate the doping behavior of Si dopant in both (100) and (010) β-Ga [...] Read more.
In this work, we train a hybrid deep-learning model (fDNN, Forest Deep Neural Network) to predict the doping level measured from the Hall Effect measurement at room temperature and to investigate the doping behavior of Si dopant in both (100) and (010) β-Ga2O3 thin film grown by the metalorganic vapor phase epitaxy (MOVPE). The model reveals that a hidden parameter, the Si supplied per nm (mol/nm), has a dominant influence on the doping process compared with other process parameters. An empirical relation is concluded from this model to estimate the doping level of the grown film with the Si supplied per nm (mol/nm) as the primary variable for both (100) and (010) β-Ga2O3 thin film. The outcome of the work indicates the similarity between the doping behavior of (100) and (010) β-Ga2O3 thin film via MOVPE and the generality of the results to different deposition systems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)
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20 pages, 3149 KiB  
Article
Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques
by Natasha Dropka, Klaus Böttcher and Martin Holena
Crystals 2021, 11(10), 1218; https://doi.org/10.3390/cryst11101218 - 9 Oct 2021
Cited by 11 | Viewed by 3234
Abstract
The aim of this study was to assess the ability of the various data mining and supervised machine learning techniques: correlation analysis, k-means clustering, principal component analysis and decision trees (regression and classification), to derive, optimize and understand the factors influencing VGF-GaAs growth. [...] Read more.
The aim of this study was to assess the ability of the various data mining and supervised machine learning techniques: correlation analysis, k-means clustering, principal component analysis and decision trees (regression and classification), to derive, optimize and understand the factors influencing VGF-GaAs growth. Training data were generated by Computational Fluid Dynamics (CFD) simulations and consisted of 130 datasets with 6 inputs (growth rate and power of 5 heaters) and 5 outputs (interface position and deflection, and temperatures at various positions in GaAs). Data mining results confirmed a good dispersion of the training data without the feasibility of a dimensionality reduction. Data clustering was observed in relation to the position of the crystallization front relative to the side heaters. Based on the statistical performance criteria and training results, decision trees identified the most decisive inputs and their ranges for a favorable interface shape and to keep GaAs temperature beyond limits for heavy arsenic evaporation. Decision trees are a recommendable machine learning technique with short training times and acceptable predictive accuracy based on small volume of CFD training data, capable of providing guidelines for understanding the crystal growth process, which is a prerequisite for the growth of low-cost, high-quality bulk crystals. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)
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26 pages, 4886 KiB  
Article
Crystal Structure Prediction of the Novel Cr2SiN4 Compound via Global Optimization, Data Mining, and the PCAE Method
by Tamara Škundrić, Dejan Zagorac, Johann Christian Schön, Milan Pejić and Branko Matović
Crystals 2021, 11(8), 891; https://doi.org/10.3390/cryst11080891 - 30 Jul 2021
Cited by 16 | Viewed by 2570
Abstract
A number of studies have indicated that the implementation of Si in CrN can significantly improve its performance as a protective coating. As has been shown, the Cr-Si-N coating is comprised of two phases, where nanocrystalline CrN is embedded in a Si3 [...] Read more.
A number of studies have indicated that the implementation of Si in CrN can significantly improve its performance as a protective coating. As has been shown, the Cr-Si-N coating is comprised of two phases, where nanocrystalline CrN is embedded in a Si3N4 amorphous matrix. However, these earlier experimental studies reported only Cr-Si-N in thin films. Here, we present the first investigation of possible bulk Cr-Si-N phases of composition Cr2SiN4. To identify the possible modifications, we performed global explorations of the energy landscape combined with data mining and the Primitive Cell approach for Atom Exchange (PCAE) method. After ab initio structural refinement, several promising low energy structure candidates were confirmed on both the GGA-PBE and the LDA-PZ levels of calculation. Global optimization yielded six energetically favorable structures and five modifications possible to be observed in extreme conditions. Data mining based searches produced nine candidates selected as the most relevant ones, with one of them representing the global minimum in the Cr2SiN4. Additionally, employing the Primitive Cell approach for Atom Exchange (PCAE) method, we found three more promising candidates in this system, two of which are monoclinic structures, which is in good agreement with results from the closely related Si3N4 system, where some novel monoclinic phases have been predicted in the past. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)
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27 pages, 5851 KiB  
Article
Boundary Conditions for Simulations of Fluid Flow and Temperature Field during Ammonothermal Crystal Growth—A Machine-Learning Assisted Study of Autoclave Wall Temperature Distribution
by Saskia Schimmel, Daisuke Tomida, Makoto Saito, Quanxi Bao, Toru Ishiguro, Yoshio Honda, Shigefusa Chichibu and Hiroshi Amano
Crystals 2021, 11(3), 254; https://doi.org/10.3390/cryst11030254 - 4 Mar 2021
Cited by 8 | Viewed by 3902
Abstract
Thermal boundary conditions for numerical simulations of ammonothermal GaN crystal growth are investigated. A global heat transfer model that includes the furnace and its surroundings is presented, in which fluid flow and thermal field are treated as conjugate in order to fully account [...] Read more.
Thermal boundary conditions for numerical simulations of ammonothermal GaN crystal growth are investigated. A global heat transfer model that includes the furnace and its surroundings is presented, in which fluid flow and thermal field are treated as conjugate in order to fully account for convective heat transfer. The effects of laminar and turbulent flow are analyzed, as well as those of typically simultaneously present solids inside the autoclave (nutrient, baffle, and multiple seeds). This model uses heater powers as a boundary condition. Machine learning is applied to efficiently determine the power boundary conditions needed to obtain set temperatures at specified locations. Typical thermal losses are analyzed regarding their effects on the temperature distribution inside the autoclave and within the autoclave walls. This is of relevance because autoclave wall temperatures are a convenient choice for setting boundary conditions for simulations of reduced domain size. Based on the determined outer wall temperature distribution, a simplified model containing only the autoclave is also presented. The results are compared to those observed using heater-long fixed temperatures as boundary condition. Significant deviations are found especially in the upper zone of the autoclave due to the important role of heat losses through the autoclave head. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)
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13 pages, 3394 KiB  
Article
Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks
by Natasha Dropka, Stefan Ecklebe and Martin Holena
Crystals 2021, 11(2), 138; https://doi.org/10.3390/cryst11020138 - 29 Jan 2021
Cited by 5 | Viewed by 2458
Abstract
The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations. Real [...] Read more.
The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations. Real time predictions of the temperatures and solid–liquid interface position in GaAs are crucial for control applications and for process visualization, i.e., for generation of digital twins. In the reported study, an LSTM network was trained on 1950 datasets with 2 external inputs and 6 outputs. Based on network performance criteria and training results, LSTMs showed the very accurate predictions of the VGF-GaAs growth process with median root-mean-square-error (RMSE) values of 2 × 10−3. This deep learning method achieved a superior predictive accuracy and timeliness compared with more traditional Nonlinear AutoRegressive eXogenous (NARX) recurrent networks. Full article
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)
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