Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks
The crystal structures of orthosilicate cathode materials play a critical role in determining the physical and chemical properties of Li-ion batteries. Accurate predictions of these crystal structures are essential for estimating key properties of cathode materials in battery applications. In this s...
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MDPI AG
2024-12-01
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| Series: | Batteries |
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| author | Mookala Premasudha Bhumi Reddy Srinivasulu Reddy Kwon-Koo Cho Ahn Hyo-Jun Jae-Kyung Sung Nagireddy Gari Subba Reddy |
| author_facet | Mookala Premasudha Bhumi Reddy Srinivasulu Reddy Kwon-Koo Cho Ahn Hyo-Jun Jae-Kyung Sung Nagireddy Gari Subba Reddy |
| author_sort | Mookala Premasudha |
| collection | DOAJ |
| description | The crystal structures of orthosilicate cathode materials play a critical role in determining the physical and chemical properties of Li-ion batteries. Accurate predictions of these crystal structures are essential for estimating key properties of cathode materials in battery applications. In this study, we utilized crystal structure data from density functional theory (DFT) calculations, sourced from the Materials Project, to predict monoclinic and orthorhombic crystal systems in orthosilicate-based cathode-based materials with Li–Si–(Fe, Mn, Co)–O compositions. An artificial neural network (ANN) model with a 6-22-22-22-1 architecture was trained on 85% of the data and tested on the remaining 15%, achieving an impressive accuracy of 97.3%. The model demonstrated strong predictive capability, with only seven misclassifications from 267 datasets, highlighting its robustness and reliability in predicting the crystal structure of orthosilicate cathodes. To enhance interpretability and model reliability, we employed the Index of Relative Importance (I<sub>RI</sub>) to identify critical features influencing predictions. Additionally, a user-friendly graphical user interface was also developed to facilitate rapid predictions, enabling researchers to explore structural configurations efficiently and accelerating advancements in battery materials research. |
| format | Article |
| id | doaj-art-f9f90d48e97046b0a3674a46b67c1d24 |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-f9f90d48e97046b0a3674a46b67c1d242025-01-24T13:22:24ZengMDPI AGBatteries2313-01052024-12-011111310.3390/batteries11010013Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural NetworksMookala Premasudha0Bhumi Reddy Srinivasulu Reddy1Kwon-Koo Cho2Ahn Hyo-Jun3Jae-Kyung Sung4Nagireddy Gari Subba Reddy5Department of Materials Engineering and Convergence Technology, Gyeongsang National University, 501 Jinju-Daero, Jinju 52828, Republic of KoreaDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, 501 Jinju-Daero, Jinju 52828, Republic of KoreaDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, 501 Jinju-Daero, Jinju 52828, Republic of KoreaDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, 501 Jinju-Daero, Jinju 52828, Republic of KoreaDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, 501 Jinju-Daero, Jinju 52828, Republic of KoreaSchool of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, 501 Jinju-Daero, Jinju 52828, Republic of KoreaThe crystal structures of orthosilicate cathode materials play a critical role in determining the physical and chemical properties of Li-ion batteries. Accurate predictions of these crystal structures are essential for estimating key properties of cathode materials in battery applications. In this study, we utilized crystal structure data from density functional theory (DFT) calculations, sourced from the Materials Project, to predict monoclinic and orthorhombic crystal systems in orthosilicate-based cathode-based materials with Li–Si–(Fe, Mn, Co)–O compositions. An artificial neural network (ANN) model with a 6-22-22-22-1 architecture was trained on 85% of the data and tested on the remaining 15%, achieving an impressive accuracy of 97.3%. The model demonstrated strong predictive capability, with only seven misclassifications from 267 datasets, highlighting its robustness and reliability in predicting the crystal structure of orthosilicate cathodes. To enhance interpretability and model reliability, we employed the Index of Relative Importance (I<sub>RI</sub>) to identify critical features influencing predictions. Additionally, a user-friendly graphical user interface was also developed to facilitate rapid predictions, enabling researchers to explore structural configurations efficiently and accelerating advancements in battery materials research.https://www.mdpi.com/2313-0105/11/1/13density functional theoryartificial neural networkcrystal systemclassificationorthosilicateLi-ion batteries |
| spellingShingle | Mookala Premasudha Bhumi Reddy Srinivasulu Reddy Kwon-Koo Cho Ahn Hyo-Jun Jae-Kyung Sung Nagireddy Gari Subba Reddy Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks Batteries density functional theory artificial neural network crystal system classification orthosilicate Li-ion batteries |
| title | Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks |
| title_full | Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks |
| title_fullStr | Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks |
| title_full_unstemmed | Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks |
| title_short | Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks |
| title_sort | classification of the crystal structures of orthosilicate cathode materials for li ion batteries by artificial neural networks |
| topic | density functional theory artificial neural network crystal system classification orthosilicate Li-ion batteries |
| url | https://www.mdpi.com/2313-0105/11/1/13 |
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