Crystal structural prediction of perovskite materials using machine learning: A comparative study

In this study, Machine Learning (ML) techniques have been exploited to classify the crystal structure of ABO3 perovskite compounds. In the present work, seven different ML algorithms are applied to the experimentally determined crystal structure data. The relevance of the data featured is measured b...

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Bibliographic Details
Published in:Solid state communications Vol. 361; p. 115062
Main Authors: Priyadarshini, Rojalina, Joardar, Hillol, Bisoy, Sukant Kishoro, Badapanda, Tanmaya
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.02.2023
Subjects:
Boosting algorithms
K-nearest neighborhood
Machine learning
Perovskite
XGBoost
K-nearest neighborhood
Perovskite
XGBoost
Machine learning
Boosting algorithms
ISSN:0038-1098
Online Access:Get full text
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Summary:In this study, Machine Learning (ML) techniques have been exploited to classify the crystal structure of ABO3 perovskite compounds. In the present work, seven different ML algorithms are applied to the experimentally determined crystal structure data. The relevance of the data featured is measured by computing the Chi-Square test and Spearman's correlation matrix. The Z-Score value has been calculated for each attribute to confirm the existence of any outliers in the data. The Synthetic Minority Oversampling (SMOTE) technique is employed to overcome the imbalanced data set. The models' performance is calculated using the stratified k-Fold cross-validation method. Further, to improve the accuracy of the prediction model, the conventional algorithm is supported by boosting algorithm. Comparative model efficiency on prediction of the crystal structure is presented to identify the most suitable model. As per the inferences drawn from the observations, the ensemble model using Xtreme Gradient Boosting (XGBoost) algorithm when applied to the pre-processed and balanced data outperforms the other models. •The prediction of crystal structure of ABO3 perovskite are implemented using various Machine Learning (ML) models.•The relevance of the features is measured by computing the Chi-Square test and Spearman's correlation matrix.•The SMOTE augmentation technique is employed to overcome the imbalanced of data set.•The models' performance is accessed using stratified k-Fold cross validation method.•The parameters obtained from various model are compared and XGBoost models is found to the most stable and accurate model.
ISSN:0038-1098
DOI:10.1016/j.ssc.2022.115062