Editorial

2 pages, 167 KiB

Open AccessEditorial

Applications of Machine Learning to the Study of Crystalline Materials

by Hartmut Schlenz and Stefan Sandfeld

Crystals 2022, 12(8), 1070; https://0-doi-org.brum.beds.ac.uk/10.3390/cryst12081070 - 30 Jul 2022

Viewed by 1177

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

(This article belongs to the Special Issue Applications of Machine Learning to the Study of Crystalline Materials)

Research

Jump to: Editorial

17 pages, 793 KiB

Open AccessEditor’s ChoiceArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/cryst12070947 - 05 Jul 2022

Cited by 6 | Viewed by 1988

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 Sr_1−xBa_x(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 Sr_1−xBa_x(Ti_1−y−zV_yFe_z)O_3−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 σ_e = 1.6 S/cm and oxygen conductivities of up to σ_i = 0.008 S/cm at T = 1173 K and an oxygen partial pressure p_O₂ = 10⁻¹⁵ bar, thus enabling practical applications. Full article

(This article belongs to the Special Issue Applications of Machine Learning to the Study of Crystalline Materials)

► Show Figures

Figure 1

20 pages, 12375 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/cryst12030372 - 10 Mar 2022

Cited by 16 | Viewed by 3339

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

(This article belongs to the Special Issue Applications of Machine Learning to the Study of Crystalline Materials)

► Show Figures

Figure 1

15 pages, 13860 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/cryst12020280 - 18 Feb 2022

Cited by 13 | Viewed by 2854

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

(This article belongs to the Special Issue Applications of Machine Learning to the Study of Crystalline Materials)

► Show Figures

Figure 1

16 pages, 2644 KiB

Open AccessEditor’s ChoiceArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/cryst11091039 - 29 Aug 2021

Cited by 3 | Viewed by 4416

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

(This article belongs to the Special Issue Applications of Machine Learning to the Study of Crystalline Materials)

► Show Figures

Figure 1

12 pages, 3584 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/cryst11091035 - 28 Aug 2021

Cited by 11 | Viewed by 3227

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

(This article belongs to the Special Issue Applications of Machine Learning to the Study of Crystalline Materials)

► Show Figures

Figure 1

15 pages, 2210 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/cryst11070818 - 14 Jul 2021

Cited by 10 | Viewed by 3079

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

(This article belongs to the Special Issue Applications of Machine Learning to the Study of Crystalline Materials)

► Show Figures

Graphical abstract

13 pages, 3618 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/cryst11030258 - 05 Mar 2021

Cited by 17 | Viewed by 3622

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

(This article belongs to the Special Issue Applications of Machine Learning to the Study of Crystalline Materials)

► Show Figures