Unveiling State-of-Charge Effects on Elastic Properties of LiCoO<sub>2</sub> via Deep Learning and Empirical Models
This study investigates the mechanical properties of LiCoO<sub>2</sub> (LCO) cathode materials under varying states of charge (SOCs) using both an empirical Buckingham potential model and a machine learning-based Deep Potential (DP) model. The results reveal a substantial decrease in You...
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2025-07-01
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| author | Ijaz Ul Haq Seungjun Lee |
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| description | This study investigates the mechanical properties of LiCoO<sub>2</sub> (LCO) cathode materials under varying states of charge (SOCs) using both an empirical Buckingham potential model and a machine learning-based Deep Potential (DP) model. The results reveal a substantial decrease in Young’s modulus with decreasing SOC. Analysis of stress factors identified pairwise interactions, particularly those involving Co<sup>3+</sup> and Co<sup>4+</sup>, as key drivers of this mechanical evolution. The DP model demonstrated superior performance by providing consistent and reliable predictions reflected in a smooth and monotonic stiffness decrease with SOC, in contrast to the large fluctuations observed in the classical Buckingham potential results. The study further identifies the increasing dominance of Co<sup>4+</sup> interactions at low SOCs as a contributor to localized stress concentrations, which may accelerate crack initiation and mechanical degradation. These findings underscore the DP model’s capability to capture SOC-dependent mechanical behavior accurately, establishing it as a robust tool for modeling battery materials. Moreover, the calculated SOC-dependent mechanical properties can serve as critical input for continuum-scale models, improving their predictive capability for chemo-mechanical behavior and degradation processes. This integrated multiscale modeling approach can offer valuable insights for developing strategies to enhance the durability and performance of lithium-ion battery materials. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
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| series | Applied Sciences |
| spelling | doaj-art-1de2eb8b8dff4e13810419ea0a41069d2025-08-20T03:35:36ZengMDPI AGApplied Sciences2076-34172025-07-011514780910.3390/app15147809Unveiling State-of-Charge Effects on Elastic Properties of LiCoO<sub>2</sub> via Deep Learning and Empirical ModelsIjaz Ul Haq0Seungjun Lee1Department of Mechanical, Robotics, and Energy Engineering, Dongguk University, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics, and Energy Engineering, Dongguk University, Seoul 04620, Republic of KoreaThis study investigates the mechanical properties of LiCoO<sub>2</sub> (LCO) cathode materials under varying states of charge (SOCs) using both an empirical Buckingham potential model and a machine learning-based Deep Potential (DP) model. The results reveal a substantial decrease in Young’s modulus with decreasing SOC. Analysis of stress factors identified pairwise interactions, particularly those involving Co<sup>3+</sup> and Co<sup>4+</sup>, as key drivers of this mechanical evolution. The DP model demonstrated superior performance by providing consistent and reliable predictions reflected in a smooth and monotonic stiffness decrease with SOC, in contrast to the large fluctuations observed in the classical Buckingham potential results. The study further identifies the increasing dominance of Co<sup>4+</sup> interactions at low SOCs as a contributor to localized stress concentrations, which may accelerate crack initiation and mechanical degradation. These findings underscore the DP model’s capability to capture SOC-dependent mechanical behavior accurately, establishing it as a robust tool for modeling battery materials. Moreover, the calculated SOC-dependent mechanical properties can serve as critical input for continuum-scale models, improving their predictive capability for chemo-mechanical behavior and degradation processes. This integrated multiscale modeling approach can offer valuable insights for developing strategies to enhance the durability and performance of lithium-ion battery materials.https://www.mdpi.com/2076-3417/15/14/7809LiCoO<sub>2</sub> (LCO)mechanical propertiesstate of charge (SOC)deep potential (DP) modellithium-ion batteries |
| spellingShingle | Ijaz Ul Haq Seungjun Lee Unveiling State-of-Charge Effects on Elastic Properties of LiCoO<sub>2</sub> via Deep Learning and Empirical Models Applied Sciences LiCoO<sub>2</sub> (LCO) mechanical properties state of charge (SOC) deep potential (DP) model lithium-ion batteries |
| title | Unveiling State-of-Charge Effects on Elastic Properties of LiCoO<sub>2</sub> via Deep Learning and Empirical Models |
| title_full | Unveiling State-of-Charge Effects on Elastic Properties of LiCoO<sub>2</sub> via Deep Learning and Empirical Models |
| title_fullStr | Unveiling State-of-Charge Effects on Elastic Properties of LiCoO<sub>2</sub> via Deep Learning and Empirical Models |
| title_full_unstemmed | Unveiling State-of-Charge Effects on Elastic Properties of LiCoO<sub>2</sub> via Deep Learning and Empirical Models |
| title_short | Unveiling State-of-Charge Effects on Elastic Properties of LiCoO<sub>2</sub> via Deep Learning and Empirical Models |
| title_sort | unveiling state of charge effects on elastic properties of licoo sub 2 sub via deep learning and empirical models |
| topic | LiCoO<sub>2</sub> (LCO) mechanical properties state of charge (SOC) deep potential (DP) model lithium-ion batteries |
| url | https://www.mdpi.com/2076-3417/15/14/7809 |
| work_keys_str_mv | AT ijazulhaq unveilingstateofchargeeffectsonelasticpropertiesoflicoosub2subviadeeplearningandempiricalmodels AT seungjunlee unveilingstateofchargeeffectsonelasticpropertiesoflicoosub2subviadeeplearningandempiricalmodels |