Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization
Significant focus has been directed towards inorganic perovskite solar cells because of their notable capabilities in converting sunlight to electricity effectively, their efficient light absorption, and their suitability for conventional semiconductor manufacturing methods. The identification of th...
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MDPI AG
2023-11-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/10/11/1232 |
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| author | Tri-Chan-Hung Nguyen Young-Un Kim Insung Jung O-Bong Yang Mohammad Shaheer Akhtar |
| author_facet | Tri-Chan-Hung Nguyen Young-Un Kim Insung Jung O-Bong Yang Mohammad Shaheer Akhtar |
| author_sort | Tri-Chan-Hung Nguyen |
| collection | DOAJ |
| description | Significant focus has been directed towards inorganic perovskite solar cells because of their notable capabilities in converting sunlight to electricity effectively, their efficient light absorption, and their suitability for conventional semiconductor manufacturing methods. The identification of the composition of perovskite materials is an ongoing challenge to achieve high performing solar cells. Conventional methods of trial and error frequently prove insufficient, especially when confronted with a multitude of potential candidates. In response to this challenge, the suggestion is to employ a machine-learning strategy for more precise and efficient prediction of the characteristics of new inorganic perovskite materials. This work utilized a dataset sourced from the Materials Project database, consisting of 1528 ABX<sub>3</sub> materials with varying halide elements (X = F, Cl, Br, Se) and information regarding their bandgap characteristics, including whether they are direct or indirect. By leveraging data augmentation and machine learning (ML) techniques along with a collection of established bandgap values and structural attributes, our proposed model can accurately and rapidly predict the bandgap of novel materials, while also identifying the key elements that contribute to this property. This information can be used to guide the discovery of new organic perovskite materials with desirable properties. Six different machine learning algorithms, including Logistic Regression (LR), Multi-layer Perceptron (MLP), Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), were used to predict the direct bandgap of potential perovskite materials for this study. RF yielded the best experimental outcomes according to the following metrics: F1-score, Recall, and Precision, attaining scores of 86%, 85%, and 86%, respectively. This result demonstrates that ML has great potential in accelerating organic perovskites material discovery. |
| first_indexed | 2024-03-09T16:31:11Z |
| format | Article |
| id | doaj.art-35c935ddb06f4b958ff16824e9db5265 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-09T16:31:11Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-35c935ddb06f4b958ff16824e9db52652023-11-24T15:01:29ZengMDPI AGPhotonics2304-67322023-11-011011123210.3390/photonics10111232Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site OptimizationTri-Chan-Hung Nguyen0Young-Un Kim1Insung Jung2O-Bong Yang3Mohammad Shaheer Akhtar4Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of KoreaGraduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of KoreaNew & Renewable Energy Material Development Center (NewREC), Jeonbuk National University, Jeonju 56332, Republic of KoreaGraduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of KoreaGraduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of KoreaSignificant focus has been directed towards inorganic perovskite solar cells because of their notable capabilities in converting sunlight to electricity effectively, their efficient light absorption, and their suitability for conventional semiconductor manufacturing methods. The identification of the composition of perovskite materials is an ongoing challenge to achieve high performing solar cells. Conventional methods of trial and error frequently prove insufficient, especially when confronted with a multitude of potential candidates. In response to this challenge, the suggestion is to employ a machine-learning strategy for more precise and efficient prediction of the characteristics of new inorganic perovskite materials. This work utilized a dataset sourced from the Materials Project database, consisting of 1528 ABX<sub>3</sub> materials with varying halide elements (X = F, Cl, Br, Se) and information regarding their bandgap characteristics, including whether they are direct or indirect. By leveraging data augmentation and machine learning (ML) techniques along with a collection of established bandgap values and structural attributes, our proposed model can accurately and rapidly predict the bandgap of novel materials, while also identifying the key elements that contribute to this property. This information can be used to guide the discovery of new organic perovskite materials with desirable properties. Six different machine learning algorithms, including Logistic Regression (LR), Multi-layer Perceptron (MLP), Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), were used to predict the direct bandgap of potential perovskite materials for this study. RF yielded the best experimental outcomes according to the following metrics: F1-score, Recall, and Precision, attaining scores of 86%, 85%, and 86%, respectively. This result demonstrates that ML has great potential in accelerating organic perovskites material discovery.https://www.mdpi.com/2304-6732/10/11/1232perovskitebandgapoptimizationfeature selectionmachine learning |
| spellingShingle | Tri-Chan-Hung Nguyen Young-Un Kim Insung Jung O-Bong Yang Mohammad Shaheer Akhtar Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization Photonics perovskite bandgap optimization feature selection machine learning |
| title | Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization |
| title_full | Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization |
| title_fullStr | Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization |
| title_full_unstemmed | Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization |
| title_short | Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization |
| title_sort | exploring data augmentation and dimension reduction opportunities for predicting the bandgap of inorganic perovskite through anion site optimization |
| topic | perovskite bandgap optimization feature selection machine learning |
| url | https://www.mdpi.com/2304-6732/10/11/1232 |
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