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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2511.15109 |
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| _version_ | 1866918209206091776 |
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| author | Chugh, Devin Sreenarayanan, Bhagath Suwito, Steven Raghavendran, Ganesh Hwang, Bing Joe Meng, Ying Shirley Su, Weinien |
| author_facet | Chugh, Devin Sreenarayanan, Bhagath Suwito, Steven Raghavendran, Ganesh Hwang, Bing Joe Meng, Ying Shirley Su, Weinien |
| contents | Machine Learning (ML) and Deep Learning (DL) based framework have evolved rapidly and generated considerable interests for predicting the properties of materials. In this work, we utilize ML-DL framework to predict the electrochemical lithiation state and associated electrical conductivity of spinel Li4Ti5O12 (LTO) thin films using Raman spectroscopy data. Raman spectroscopy, with its rapid, non-destructive, and high-resolution capabilities, is leveraged to monitor dynamic electrochemical changes in LTO films. A comprehensive dataset of 3,272 Raman spectra, representing lithiation states from 0% to 100%, was collected and preprocessed using advanced techniques including cosmic ray removal, smoothing, baseline correction, normalization, and data augmentation. Classical machine learning models such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Random Forest (RF) were evaluated alongside a Convolutional Neural Network (CNN). While traditional models achieved moderate to high accuracy, they struggled with generalization and noise sensitivity. In contrast, the CNN demonstrated superior performance, achieving over 99.5% accuracy and robust predictions on unseen samples. The CNN model effectively captured non-linear spectral features and showed resilience to experimental variability. This pipeline not only enables accurate lithiation state classification but also facilitates conductivity estimation, offering a scalable approach for real-time battery material characterization and potential extension to other spectroscopic datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15109 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning Assisted Prediction of Electrochemical Lithiation State in Spinel Lithium Titanium Oxide Thin Films Chugh, Devin Sreenarayanan, Bhagath Suwito, Steven Raghavendran, Ganesh Hwang, Bing Joe Meng, Ying Shirley Su, Weinien Materials Science Machine Learning (ML) and Deep Learning (DL) based framework have evolved rapidly and generated considerable interests for predicting the properties of materials. In this work, we utilize ML-DL framework to predict the electrochemical lithiation state and associated electrical conductivity of spinel Li4Ti5O12 (LTO) thin films using Raman spectroscopy data. Raman spectroscopy, with its rapid, non-destructive, and high-resolution capabilities, is leveraged to monitor dynamic electrochemical changes in LTO films. A comprehensive dataset of 3,272 Raman spectra, representing lithiation states from 0% to 100%, was collected and preprocessed using advanced techniques including cosmic ray removal, smoothing, baseline correction, normalization, and data augmentation. Classical machine learning models such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Random Forest (RF) were evaluated alongside a Convolutional Neural Network (CNN). While traditional models achieved moderate to high accuracy, they struggled with generalization and noise sensitivity. In contrast, the CNN demonstrated superior performance, achieving over 99.5% accuracy and robust predictions on unseen samples. The CNN model effectively captured non-linear spectral features and showed resilience to experimental variability. This pipeline not only enables accurate lithiation state classification but also facilitates conductivity estimation, offering a scalable approach for real-time battery material characterization and potential extension to other spectroscopic datasets. |
| title | Deep Learning Assisted Prediction of Electrochemical Lithiation State in Spinel Lithium Titanium Oxide Thin Films |
| topic | Materials Science |
| url | https://arxiv.org/abs/2511.15109 |