Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom Properties
Perovskite materials have high potential for the renewable energy sources such as solar PV cells, fuel cells, etc. Different structural distortions such as crystal structure and lattice parameters have a critical impact on the determination of the perovskite’s structure strength, stability, and over...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/12/11/1570 |
| _version_ | 1797468651768512512 |
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| author | Sams Jarin Yufan Yuan Mingxing Zhang Mingwei Hu Masud Rana Sen Wang Ruth Knibbe |
| author_facet | Sams Jarin Yufan Yuan Mingxing Zhang Mingwei Hu Masud Rana Sen Wang Ruth Knibbe |
| author_sort | Sams Jarin |
| collection | DOAJ |
| description | Perovskite materials have high potential for the renewable energy sources such as solar PV cells, fuel cells, etc. Different structural distortions such as crystal structure and lattice parameters have a critical impact on the determination of the perovskite’s structure strength, stability, and overall performance of the materials in the applications. To improve the perovskite performance and accelerate the prediction of different structural distortions, few ML models have been established to predict the type of crystal structures and their lattice parameters using the basic atom characteristics of the perovskite materials. In this work, different ML models such as random forest (RF), support vector machine (SVM), neural network (NN), and genetic algorithm (GA) supported neural network (GA-NN) have been established, whereas support vector regression (SVR) and genetic algorithm-supported support vector regression (GA-SVR) models have been assessed for the prediction of the lattice parameters. The prediction model accuracy for the crystal structure classification is almost 88% in average for GA-NN whereas for the lattice constants regression model GA-SVR model gives ~95% in average which can be further improved by accumulating more robust datasets into the database. These ML models can be used as an alternative process to accelerate the development of finding out new perovskite material by providing valuable insight for the behaviours of the perovskite materials. |
| first_indexed | 2024-03-09T19:10:26Z |
| format | Article |
| id | doaj.art-7e5cf33ab0904fc49171dd967c912025 |
| institution | Directory Open Access Journal |
| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-03-09T19:10:26Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-7e5cf33ab0904fc49171dd967c9120252023-11-24T04:15:25ZengMDPI AGCrystals2073-43522022-11-011211157010.3390/cryst12111570Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom PropertiesSams Jarin0Yufan Yuan1Mingxing Zhang2Mingwei Hu3Masud Rana4Sen Wang5Ruth Knibbe6School of Mechanical and Mining Engineering, University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Mechanical and Mining Engineering, University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Mechanical and Mining Engineering, University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Mechanical and Mining Engineering, University of Queensland, Brisbane, QLD 4072, AustraliaNanomaterials Centre, Australian Institute for Bioengineering and Nanotechnology, Brisbane, QLD 4072, AustraliaSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Mechanical and Mining Engineering, University of Queensland, Brisbane, QLD 4072, AustraliaPerovskite materials have high potential for the renewable energy sources such as solar PV cells, fuel cells, etc. Different structural distortions such as crystal structure and lattice parameters have a critical impact on the determination of the perovskite’s structure strength, stability, and overall performance of the materials in the applications. To improve the perovskite performance and accelerate the prediction of different structural distortions, few ML models have been established to predict the type of crystal structures and their lattice parameters using the basic atom characteristics of the perovskite materials. In this work, different ML models such as random forest (RF), support vector machine (SVM), neural network (NN), and genetic algorithm (GA) supported neural network (GA-NN) have been established, whereas support vector regression (SVR) and genetic algorithm-supported support vector regression (GA-SVR) models have been assessed for the prediction of the lattice parameters. The prediction model accuracy for the crystal structure classification is almost 88% in average for GA-NN whereas for the lattice constants regression model GA-SVR model gives ~95% in average which can be further improved by accumulating more robust datasets into the database. These ML models can be used as an alternative process to accelerate the development of finding out new perovskite material by providing valuable insight for the behaviours of the perovskite materials.https://www.mdpi.com/2073-4352/12/11/1570machine learning (ML)perovskitescrystal structureslattice parametersfeature scalingfeature correlations |
| spellingShingle | Sams Jarin Yufan Yuan Mingxing Zhang Mingwei Hu Masud Rana Sen Wang Ruth Knibbe Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom Properties Crystals machine learning (ML) perovskites crystal structures lattice parameters feature scaling feature correlations |
| title | Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom Properties |
| title_full | Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom Properties |
| title_fullStr | Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom Properties |
| title_full_unstemmed | Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom Properties |
| title_short | Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom Properties |
| title_sort | predicting the crystal structure and lattice parameters of the perovskite materials via different machine learning models based on basic atom properties |
| topic | machine learning (ML) perovskites crystal structures lattice parameters feature scaling feature correlations |
| url | https://www.mdpi.com/2073-4352/12/11/1570 |
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