Applications of Machine Learning to the Study of Crystalline Materials

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

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 25828

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Guest Editor
Institute of Energy and Climate Research (IEK), IEK-1: Materials Synthesis and Processing, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Interests: crystallography; thermodynamics; inorganic materials; amorphous structures; ceramics; X-ray diffraction; synchrotron radiation; Raman spectroscopy; machine learning; research data management

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Guest Editor
Institut for Advanced Simulation (IAS), IAS-9: Materials Data Science and Informatics, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Interests: computational materials; artificial intelligence; crystalline defects; simulation; microstructure evolution; structure-property relation

Special Issue Information

Dear Colleagues,

Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics, chemistry, materials science and structure research. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, analyse crystal structures and the related properties, and generally accelerate the discovery of new materials. Thus, one goal of this special issue is to demonstrate available, practical machine learning techniques that can be used to study crystalline materials today, by means of the application of different ML techniques (including Deep Learning) as well as by the demonstration of best practices. The focus will be on the practical application of ML in materials research in order to inspire more materials scientists and crystallographers to use ML as a powerful tool in research and also to demonstrate the potential benefits of ML as well as to improve communication between theoretically and more practically working scientists, in order to reduce any inhibitions that may exist. In this context, one goal may be to establish ML as a way to usefully extend existing analytical procedures and also to obtain results that cannot be obtained by experiments and standard procedures, or only at a disproportionately high cost.

Prof. Dr. Hartmut Schlenz
Prof. Dr. Stefan Sandfeld
Guest Editors

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Keywords

  • crystalline materials
  • ceramics
  • machine learning
  • deep learning
  • crystal engineering
  • materials informatics
  • structure research
  • best practices

Published Papers (8 papers)

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Editorial

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2 pages, 167 KiB  
Editorial
Applications of Machine Learning to the Study of Crystalline Materials
by Hartmut Schlenz and Stefan Sandfeld
Crystals 2022, 12(8), 1070; https://doi.org/10.3390/cryst12081070 - 30 Jul 2022
Viewed by 1164
Abstract
This Special Issue, “Applications of Machine Learning to the Study of Crystalline Materials”, is a collection of seven original articles published in 2021 and 2022 and dedicated to applications of machine learning in materials research [...] Full article

Research

Jump to: Editorial

17 pages, 793 KiB  
Article
The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning
by Hartmut Schlenz, Stefan Baumann, Wilhelm Albert Meulenberg and Olivier Guillon
Crystals 2022, 12(7), 947; https://doi.org/10.3390/cryst12070947 - 05 Jul 2022
Cited by 6 | Viewed by 1982
Abstract
The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system Sr1−xBax(Ti [...] Read more.
The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system Sr1−xBax(Ti1−y−zVyFez)O3−δ (cubic perovskite-type phases). We have evaluated available experimental data, determined missing crystallographic information using bond-valence modeling and programmed a Python code to be able to generate training data sets for property predictions using machine learning. Indeed, suitable compositions of cubic perovskite-type phases can be predicted in this way, allowing for larger electronic conductivities of up to σe = 1.6 S/cm and oxygen conductivities of up to σi = 0.008 S/cm at T = 1173 K and an oxygen partial pressure pO2 = 10−15 bar, thus enabling practical applications. Full article
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20 pages, 12375 KiB  
Article
Artificial Neural Network Modeling to Predict the Effect of Milling Time and TiC Content on the Crystallite Size and Lattice Strain of Al7075-TiC Composites Fabricated by Powder Metallurgy
by Mohammad Azad Alam, Hamdan H. Ya, Mohammad Azeem, Mohammad Yusuf, Imtiaz Ali Soomro, Faisal Masood, Imtiaz Ahmed Shozib, Salit M. Sapuan and Javed Akhter
Crystals 2022, 12(3), 372; https://doi.org/10.3390/cryst12030372 - 10 Mar 2022
Cited by 16 | Viewed by 3318
Abstract
In the study, Al7075-TiC composites were synthesized by using a novel dual step blending process followed by cold pressing and sintering. The effect of ball milling time on the microstructure of the synthesized composite powder was characterized using X-ray diffraction measurements (XRD), scanning [...] Read more.
In the study, Al7075-TiC composites were synthesized by using a novel dual step blending process followed by cold pressing and sintering. The effect of ball milling time on the microstructure of the synthesized composite powder was characterized using X-ray diffraction measurements (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). Subsequently, the integrated effects of the two-stage mechanical alloying process were investigated on the crystallite size and lattice strain. The crystallite size and lattice strain of blended samples were calculated using the Scherrer method. The prediction of the crystallite size and lattice strain of synthesized composite powders was conducted by an artificial neural network technique. The results of the mixed powder revealed that the particle size and crystallite size improved with increasing milling time. The particle size of the 3 h-milled composites was 463 nm, and it reduces to 225 nm after 7 h of milling time. The microhardness of the produced composites was significantly improved with milling time. Furthermore, an artificial neuron network (ANN) model was developed to predict the crystallite size and lattice strain of the synthesized composites. The ANN model provides an accurate model for the prediction of lattice parameters of the composites. Full article
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15 pages, 13860 KiB  
Article
Grain Knowledge Graph Representation Learning: A New Paradigm for Microstructure-Property Prediction
by Chao Shu, Junjie He, Guangjie Xue and Cheng Xie
Crystals 2022, 12(2), 280; https://doi.org/10.3390/cryst12020280 - 18 Feb 2022
Cited by 13 | Viewed by 2841
Abstract
The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been used to predict the properties of polycrystalline materials. However, they cannot capture the complex interactive [...] Read more.
The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been used to predict the properties of polycrystalline materials. However, they cannot capture the complex interactive relationship between the grains in the microstructure, which is a crucial factor affecting the material’s macroscopic properties. Here, we propose a grain knowledge graph representation learning method. First, based on the polycrystalline structure, an advanced digital representation of the knowledge graph is constructed, embedding ingenious knowledge while completely restoring the polycrystalline structure. Then, a heterogeneous grain graph attention model (HGGAT) is proposed to realize the effective high-order feature embedding of the microstructure and to mine the relationship between the structure and the material properties. Through benchmarking with other machine learning methods on magnesium alloy datasets, HGGAT consistently demonstrates superior accuracy on different performance labels. The experiment shows the rationality and validity of the grain knowledge graph representation and the feasibility of this work to predict the material’s structural characteristics. Full article
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16 pages, 2644 KiB  
Article
Crystal-Site-Based Artificial Neural Networks for Material Classification
by Juan I. Gómez-Peralta, Nidia G. García-Peña and Xim Bokhimi
Crystals 2021, 11(9), 1039; https://doi.org/10.3390/cryst11091039 - 29 Aug 2021
Cited by 3 | Viewed by 4406
Abstract
In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the [...] Read more.
In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures. Full article
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12 pages, 3584 KiB  
Article
Stepwise Evolution of Photocatalytic Spinel-Structured (Co,Cr,Fe,Mn,Ni)3O4 High Entropy Oxides from First-Principles Calculations to Machine Learning
by Chia-Chun Lin, Chia-Wei Chang, Chao-Cheng Kaun and Yen-Hsun Su
Crystals 2021, 11(9), 1035; https://doi.org/10.3390/cryst11091035 - 28 Aug 2021
Cited by 11 | Viewed by 3219
Abstract
High entropy oxides (HEOx) are novel materials, which increase the potential application in the fields of energy and catalysis. However, a series of HEOx is too novel to evaluate the synthesis properties, including formation and fundamental properties. Combining first-principles calculations with machine learning [...] Read more.
High entropy oxides (HEOx) are novel materials, which increase the potential application in the fields of energy and catalysis. However, a series of HEOx is too novel to evaluate the synthesis properties, including formation and fundamental properties. Combining first-principles calculations with machine learning (ML) techniques, we predict the lattice constants and formation energies of spinel-structured photocatalytic HEOx, (Co,Cr,Fe,Mn,Ni)3O4, for stoichiometric and non-stoichiometric structures. The effects of site occupation by different metal cations in the spinel structure are obtained through first-principles calculations and ML predictions. Our predicted results show that the lattice constants of these spinel-structured oxides are composition-dependent and that the formation energies of those oxides containing Cr atoms are low. The computing time and computing energy can be greatly economized through the tandem approach of first-principles calculations and ML. Full article
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15 pages, 2210 KiB  
Article
Predicting Perovskite Performance with Multiple Machine-Learning Algorithms
by Ruoyu Li, Qin Deng, Dong Tian, Daoye Zhu and Bin Lin
Crystals 2021, 11(7), 818; https://doi.org/10.3390/cryst11070818 - 14 Jul 2021
Cited by 10 | Viewed by 3069
Abstract
Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, [...] Read more.
Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials. Full article
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13 pages, 3618 KiB  
Article
Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling
by Patrick Trampert, Dmitri Rubinstein, Faysal Boughorbel, Christian Schlinkmann, Maria Luschkova, Philipp Slusallek, Tim Dahmen and Stefan Sandfeld
Crystals 2021, 11(3), 258; https://doi.org/10.3390/cryst11030258 - 05 Mar 2021
Cited by 17 | Viewed by 3600
Abstract
The analysis of microscopy images has always been an important yet time consuming process in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount [...] Read more.
The analysis of microscopy images has always been an important yet time consuming process in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount of hand annotated data, which can be a problem for material science data. We present a procedure to generate synthetic data based on ad hoc parametric data modelling for enhancing generalization of trained neural network models. Especially for situations where it is not possible to gather a lot of data, such an approach is beneficial and may enable to train a neural network reasonably. Furthermore, we show that targeted data generation by adaptively sampling the parameter space of the generative models gives superior results compared to generating random data points. Full article
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